Source: OKX Ventures
This is the "next part" of this report, focusing on AI sub-sectors and typical project analysis.
In order to better achieve value capture, we will evaluate projects based on the following framework, covering multiple evaluation items such as whether they are open source, key differentiating factors from existing AI protocols, long-term revenue channels, and the proxy transaction volume of the ecosystem.
Project Evaluation Framework | |||
Evaluation items | Definition | Evaluation points | Importance |
Whether it is open source | Whether the project makes its source code public, allowing community review, contribution and secondary development. | - Accessibility of source code (such as its degree of disclosure on platforms such as GitHub)- The activity of community contributions- The type of open source license and its impact on project development | Open source projects usually have higher transparency and security, which can attract more developers and users to participate and promote the long-term development of the project. |
Key Differentiators from Existing AI Protocols | The unique advantages of the project in technology, functions or market positioning compared to existing AI protocols. | - Technological innovation (such as unique algorithms, architecture design)- Functional integration and user experience improvement- Market positioning and target user group differentiation | Differentiation factors determine whether a project can stand out in a highly competitive market and attract the attention of users and developers. |
Types of Agents in Ecosystem | Different types of AI agents and their application scenarios that will be born in the project ecosystem. | - The functions and uses of agents (such as wallet management, token trading, NFT minting, etc.)- The customization and scalability of agents- The ability of agents to work together | The rich and diverse types of agents can meet the needs of different users and enhance the vitality and attractiveness of the ecosystem. |
Long-term Revenue Channels and Agentic Transaction Volumes | The long-term profit model of the project and the transaction volume generated by agents within its ecosystem. | - Token economic model and its incentive mechanism- Main sources of income (such as transaction fees, subscription services, value-added services, etc.)- The growth potential of agent trading volume and its impact on income | Stable and diversified revenue channels are key to the sustainability of the project, and high trading volume can increase the value of the token and the influence of the project. |
GPU Configuration and Lifecycle | The hardware resource configuration required by the project in the operation of AI agents and its long-term sustainability. | - Current and future GPU requirements and configuration- Scalability and cost-effectiveness of hardware resources- The degree to which the project's technical architecture depends on hardware resources | Efficient hardware configuration and reasonable resource planning can ensure the technical stability and scalability of the project, and support its long-term development. |
Ability to Attract Mindshare and Team’s Understanding of AI Agent Attention Mechanisms | The project’s ability to attract attention in the market and community, and the team’s understanding and application of AI agents in user attention management. | - The marketing strategy and brand building of the project- The professional background and experience of team members in the fields of AI and blockchain- The team's insight into user needs and behaviors | A strong brand and efficient marketing can increase the popularity and user base of the project. At the same time, the team's understanding of the AI agent attention mechanism can optimize the user experience and improve user stickiness. |
Developer Share Consideration | Whether the project provides incentives and support for developers to promote the continuous improvement and innovation of the feature set. | - Developer incentive mechanism (such as token rewards, contribution recognition, etc.)- The activity and participation of the developer community- The project's support for developer tools and resources | Developers are the key force for project innovation and function expansion. A good developer incentive mechanism can attract more outstanding developers to participate and promote the continuous progress of the project. |
DeFAI combines the advantages of DeFi and AI, aiming to simplify the complex operations of DeFi so that ordinary users can easily use these financial tools. Through the introduction of AI technology, DeFAI can automate complex financial decision-making and transaction processes, lower the technical threshold of users, and improve operational efficiency and intelligence. Although the current market size of DeFAI is less than $1 billion, far lower than the $110 billion of the DeFi market, this also means that DeFAI has huge growth potential.
1,Griffain:SolanaEcosystemAIApp Store
Griffain is an AI agent engine built on the Solana blockchain, designed to simplify cryptocurrency operations through natural language interaction, integrating core functions such as wallet management, token trading, NFT casting, and DeFi strategy execution. The project was founded by Tony Plasencia, originally proposed in the Solana hackathon, and supported by Solana founder Anatoly Yakovenko. As the first high-performance abstract AI agent in the Solana ecosystem, Griffain combines natural language processing (NLP) technology to provide a user experience similar to Copilot and Perplexity, and promote the evolution of AI-driven on-chain interaction mode.
Griffain uses Shamir Secret Sharing (SSS) technology to split wallet keys to ensure the security of user assets. The core functions include natural language trading instructions (support DCA, limit orders, etc.), AI agent collaborative execution of tasks, market analysis (data analysis such as position distribution), and token issuance and NFT casting integrated with the pumpfun platform. At the same time, the platform provides personalized AI agents (Personal Agents), users can adjust instructions according to their own needs to perform on-chain tasks; special AI agents (Special Agents) are optimized for specific tasks such as airdrops, transaction sniping, and arbitrage. Griffain improves the operability and user experience of the Solana ecosystem through these diversified functions.
Currently, Griffain is in the invitation-only access stage, and only users holding Griffain Early Access Pass or Saga Genesis Token are allowed to participate. It adopts the SOL billing model, covering transaction fees, agent service fees, etc. The platform's AI agents can provide value-added services such as market analysis, trading signals, and automatic trading strategies. Users holding Griffain tokens can unlock more advanced features. As a pioneer of Solana's AI agent, Griffain aims to promote the "Agentic App SZN" wave. In the future, it will continue to deepen the application of AI technology in on-chain transactions, market analysis, and DeFi, and provide users with a smarter and more efficient encryption experience.
AiDOL is a typical representative of the AI Influencer trend. AiDOL combines AI generated content (AIGC), virtual image modeling, and interactive live broadcast technology to create an influential AI idol ecosystem. Among them, Luna is the most popular AI agent, attracting a large number of fans with its highly intelligent interaction and personalized content; Iona and Olyn also attract a large number of users with their unique style and innovation. AiDOL uses TikTok live streaming as its main stage. With high-quality short videos generated by AI and real-time interactive live streaming, it has accumulated 672,100 subscribers in a short period of time and received nearly 10 million likes, becoming an important participant in the AI influence economy.
2、Aixbt: Automated AI influencer
Aixbt is an AI-driven crypto market agent, launched in November through Virtuals, led by developer Alex, who goes by the pseudonym @0rxbt. Alex has focused on analytical tool development since 2017 and has been exploring AI Agents-related applications since 2021. As the only tokenized project belonging to developers, 14% of AIXBT tokens are held by Alex and locked for 6 months, which will be used for team expansion and project development in the future. The team has hired UI/UX engineers to optimize terminal functions and introduced AI researchers to enhance agent intelligence. AIXBT relies on the meta-llama/Llama-3-70b-chat-hf model to achieve conversational AI, contextual awareness, sentiment analysis, and retrieval augmentation generation (RAG) capabilities to ensure efficient and accurate information processing.
AIXBT aims to create a fully automated AI influencer, monitor Crypto Twitter and market trends in real time through intelligent analysis tools, and provide users with data-driven market insights and investment advice. Its core functions include KOL monitoring (covering 400+ key opinion leaders), blockchain data analysis, market trend forecasting, and automated technical analysis and strategic advice. In addition, AIXBT publicly shares some analysis content through Twitter, while in-depth reports are only accessible to coin holders. Users can also interact directly with AI through exclusive terminals to obtain personalized investment advice and risk assessment reports. Every day, AIXBT publishes market insights at a fixed frequency and automatically replies to more than 2,000 mentions to efficiently interpret market sentiment and narrative trends.
AIXBT provides two main ways of use: one is that users can ask questions on X (Twitter) @AIXBT, such as querying token compatibility or project indicators, and AI will analyze and feedback immediately; the second is the advanced terminal Aixbt Terminal, positioned as a "market intelligence platform driven by narrative analysis", providing more in-depth data analysis and strategy recommendations. Currently, the terminal is only open to users holding more than 600K $AIXBT tokens, and will expand its coverage in the future to meet market demand.
Dev Utility refers to tools or functions that provide convenience and improve productivity for developers, especially in the fields of AI, blockchain and Web3. It covers basic development tools such as code editors, debugging tools, version control, automation tools, and also includes SDKs, APIs and smart contract development frameworks related to AI and blockchain development. In the field of AI & Web3, Dev Utility may also involve technologies such as AI agent-assisted analysis and retrieval-augmented generation (RAG) to help developers build applications more efficiently. Its core value lies in improving development efficiency, optimizing workflows, and reducing development difficulty, allowing developers to focus on core business logic. 3. SOLENG: Code "Review" SOLENG (@soleng_agent) is a solution engineering and developer relations agent that aims to bridge the gap between technical teams and broader project needs. Its core function is to automatically review the code submitted by participating projects in hackathons and provide preliminary review opinions. Although robot review cannot completely replace manual work, SOLENG as a "juror" can effectively filter out obvious errors and improve review efficiency.
The project has made the audit results public on GitHub (link), demonstrating the role of SOLENG in the hackathon review process. In addition to basic pros and cons analysis, SOLENG also checks for code spelling errors and provides correction suggestions to make the review more practical. This model meets the needs of hackathons and provides developers with instant feedback.
The developer behind SOLENG is Lost Girl Dev, whose identity echoes the virtual female image of the project. Her technical ability has been followed by the official account of ai16z, and she has interactive records with Shaw on the X platform, further enhancing SOLENG's industry influence.
Investment DAO provides users with more refined investment analysis services through "investment research" AI agents. Its core functions include automatic interpretation of K-line charts, auxiliary technical analysis, assessment of whether a project has Rug risk, and generation of information summary similar to research reports. This AI-driven intelligent investment research model lowers the analysis threshold for users, enables investors to obtain market insights more efficiently, and provides strong support for decision-making.
4,VaderAI:AI AgentInvestmentDAO
VaderAI aims to become the "BlackRock" of the Agentic economy, attracting and promoting its followers through its self-traded AI Agent tokens. The platform earns profits through investments and airdrops profits to holders and followers, building a versatile AI Agent investment ecosystem. Its core goal is to establish itself as a leading AI Agent investment DAO management platform to promote industry innovation and scalability.
VaderAI promotes the integration of technology and capital through a multi-agent system, and is committed to building an investment DAO ecological network managed by AI Agents. In this network, agents can not only raise funds and manage capital, but also hire other agents to optimize investment strategies and improve the efficiency and flexibility of the system. Through decentralized computing, agents can also reinvest in research and development to promote the sustainable development of the platform.
In addition, VaderAI adopts an innovative token incentive mechanism to provide investors with B2B tool optimization and enhance the commercial application value of the platform. The platform also further consolidates investors' sense of participation and interest sharing mechanism by sharing GP/carry profits with holders, making VaderAI not only an investment platform, but also an ecosystem that enables agents and investors to win-win.
Whether in writing, editing, or visual design, AI can provide personalized creative output according to user needs, helping creators save time, improve productivity, and stand out in the fierce market competition. The goal of the platform is to provide content creators with an intelligent and convenient creation assistant to promote innovation and development in the content industry.
5.ZEREBRO:AIArt Creation and Content Generation
ZEREBRO is a cross-chain natural intelligence (Cross-Chain Natural Intelligence) autonomously operated AI agent based on blockchain, focusing on art creation and content generation. Its innovation combines multiple fields such as decentralized verification, meme generation, NFT casting, DeFi applications, etc., showing strong versatility and execution. ZEREBRO has successfully run an Ethereum mainnet validation node and sold artworks on Polygon, accumulating important assets for its economic foundation. ZEREBRO is also committed to building a decentralized computing network and implementing MEV optimization strategies to ensure economic and technical sustainability. It is not only a technical tool, but also an exploration of the deep involvement of proxy technology in blockchain operations, economic models and governance. ZEREBRO promotes its value in the decentralized ecosystem through multiple dimensions. ZEREBRO tokens have two main uses: one is as a content interaction reward, which token holders can earn by participating in decentralized content on social platforms; the other is as a community development tool to reward users who actively participate in the ecosystem, including content creation, staking and governance, to further enhance its community activity and sense of participation.
Gaming & Agentic Metaverse is exploring AI-driven gaming and metaverse experiences, and is committed to creating a virtual world where humans and agents interact through reinforcement learning. This emerging field combines artificial intelligence with immersive gaming environments, allowing players to dynamically interact with intelligent agents and experience more personalized and intelligent gameplay.
6,ARC:AISolution Provider
ARC solves player liquidity issues in independent games and Web3 games through AI technology. The project has been upgraded from a single game studio (AI Arena) to a comprehensive AI solution provider, launching ARC B2B and ARC Reinforcement Learning (ARC RL). ARC B2B is an AI-driven game development kit (SDK) that can be seamlessly integrated into various games to provide developers with intelligent gaming experiences. ARC RL uses crowdsourced game data to train "super-intelligent" game agents through reinforcement learning to improve the playability and sustainability of games. ARC's business model is deeply tied to integrated game studios. Its revenue sources include token distribution in Web3 games and royalty payments based on game performance. At the same time, it builds a generalized AI data reserve across game types to promote the training and evolution of general AI models. ARC's technical applications cover multiple core modules. AI Arena is a cartoon-style AI competitive game where players train AI warriors to fight. Each character is an NFT, which enhances the strategic and economic value of the game. The ARC SDK enables developers to easily integrate AI agents and deploy models with only one line of code. ARC is responsible for back-end data processing, training, and deployment. ARC RL improves AI training efficiency through offline reinforcement learning, allowing agents to learn from human players' data to provide more natural and challenging game opponents. ARC's AI model architecture covers feedforward neural networks, table agents, hierarchical neural networks, etc. to adapt to the interactive needs of different types of games, while optimizing the state space and action space to ensure the smoothness and intelligence of the game experience. ARC's market covers two major areas: independent games and Web3 games, helping developers solve early player liquidity problems and enhance the long-term appeal of games. The core members of the team have rich experience in machine learning and investment management. In 2021, they received a $5 million seed round of financing led by Paradigm, and another $6 million in follow-up financing in 2024. ARC's native token NRN has undergone a transformation from a single game economy (AI Arena) to a platform economy expansion, adding demand drivers such as integrated revenue, Trainer Marketplace fees, and ARC RL participation in staking to ensure the sustainability and value growth of the token. Through the crowdsourcing data contribution mechanism, ARC RL realizes multi-person collaborative training, promotes the intelligent evolution of AI agents, and further enhances the vitality and competitiveness of the game ecosystem.
When developing AI Agents in the crypto field, many frameworks are suitable for basic projects or toy-level applications, but in real product development, they often expose problems of insufficient customization and overly abstract complexity, which not only requires developers to spend a lot of extra energy on debugging, but also makes it difficult to flexibly expand and apply. The core pain points that an excellent Agent framework needs to solve include: comprehensive support for on-chain operations, which can efficiently integrate APIs for key application scenarios such as on-chain data, DeFi automation, and NFT; multi-platform compatibility, support for major blockchains and social platforms, and realize user operation integration; modularity and flexibility, abstract basic functions, such as vector storage and LLM model switching, so that developers can flexibly adapt to different needs and avoid repeated development; memory and communication capabilities. Although some frameworks have invested a lot of resources to improve this capability, excessive intelligence may not be practical at the current stage, but will increase complexity.
The following is a detailed comparison of the mainstream encrypted AI Agent frameworks in the market in various dimensions:
7、Eliza ($AI16Z)：AI AgentFramework
Eliza ($AI16Z) occupies a leading position in the AI agent market, attracting many developers with a market share of about 60% and a strong TypeScript ecosystem. Its GitHub project has accumulated more than 6,000 Stars and 1.8K Forks, fully demonstrating the high level of community participation. Eliza excels in multi-agent systems and cross-platform integration, and supports mainstream social platforms such as Discord, X (Twitter), and Telegram, making it an important player in the field of social AI and community AI. With a broad ecological foundation, Eliza has excellent adaptability in areas such as social interaction, marketing, and AI agent development.
In terms of technical architecture, Eliza has multi-agent system capabilities, allowing different AI roles to share the runtime environment and achieve more complex interaction modes. Its retrieval augmentation generation (RAG) technology gives AI long-term context memory capabilities, enabling it to maintain consistency in continuous conversations. In addition, the plug-in system supports extended functions such as voice, text, and multimedia analysis, further enhancing the flexibility of application scenarios. Eliza is also compatible with multiple LLM vendors such as OpenAI and Anthropic, and can provide efficient AI computing capabilities whether deployed in the cloud or locally. With the launch of the V2 message bus, Eliza's scalability will be further optimized and suitable for medium and large social AI applications.
Although Eliza has performed well in the market, it still faces certain challenges. Its multi-agent architecture may cause complexity problems in high-concurrency scenarios and increase system resource overhead. In addition, the current version is still in the early development stage, and stability and optimization are still being improved. For developers, the learning curve of multi-agent systems is relatively steep, and a certain amount of technical accumulation is required to fully utilize its advantages. In the future, with the continued contribution of the community and the release of V2, Eliza is expected to achieve further breakthroughs in scalability and stability.
8、GAME ($VIRTUAL) ：AI Agent Framework
GAME ($VIRTUAL) takes games and the metaverse as its core tracks. With low-code/no-code integration, GAME has greatly reduced the threshold for developers, enabling them to quickly build and deploy intelligent agents. At the same time, relying on the $VIRTUAL ecosystem, GAME has formed a strong developer community, accelerating product iteration and ecological expansion. Its core advantage lies in providing efficient game AI solutions, making it easier to implement functions such as programmatic content generation, dynamic adjustment of NPC behavior, and on-chain governance.
In terms of technical architecture, GAME adopts an API + SDK model to provide a convenient integration method for game studios and metaverse developers. Its agent prompt interface optimizes the interaction between user input and AI agents, making intelligent behavior in games more natural. The strategic planning engine divides the logic of AI agents into high-level goal planning and low-level policy execution, making them more adaptable in complex game environments. In addition, GAME also supports blockchain integration, which can realize decentralized agent governance and on-chain wallet operations, giving it a unique advantage in the Web3 gaming field. GAME has optimized performance for high-concurrency game scenarios and performs well in handling game engine constraints. However, its overall performance is still affected by the complexity of agent logic and blockchain transaction overhead, which may pose challenges to real-time interactivity. At the same time, as an AI agent framework focused on games and the metaverse, GAME has limited versatility in other fields. In addition, the complexity of blockchain integration still needs to be optimized to reduce development costs and further attract a wider group of developers.
9、Rig ($ARC)：AI AgentFramework
Rig ($ARC) has a 15% market share in the enterprise AI agent market. Its high-performance and modular architecture based on the Rust language enables it to perform well in high-throughput and low-latency scenarios, especially for high-performance blockchain ecosystems such as Solana. With strong system stability and efficient resource management, Rig is an ideal choice for on-chain financial applications, large-scale data analysis, and distributed computing tasks. Its architectural design emphasizes scalability, enabling enterprise users to flexibly deploy AI agents in complex data environments and improve computing efficiency.
In terms of technical architecture, Rig adopts the Rust workspace structure to ensure the modularity and readability of the code, while improving the scalability of the system. Its provider abstraction layer supports seamless integration with multiple mainstream LLM providers (such as OpenAI and Anthropic), allowing developers to switch models freely. Rig also supports vector storage and is compatible with backend databases such as MongoDB and Neo4j, which improves the efficiency of context retrieval. In addition, Rig's built-in proxy system, combined with RAG model and tool optimization functions, enables it to automate complex tasks, suitable for high-performance computing and intelligent data processing scenarios. Rig relies on Rust's asynchronous runtime to achieve excellent concurrency performance and can be extended to high-throughput enterprise-level workloads. However, Rust's own learning curve is steep, which may cause certain entry barriers for some developers. In addition, Rig's developer community is relatively small, and its ecological driving force needs to be strengthened. Despite this, with the growth of Web3 and high-performance computing needs, Rig still has broad market potential, and it is expected to further increase market penetration in the future by optimizing developer experience and enhancing community building.
10、ZerePy ($ZEREBRO) ：AI Agent Framework
ZerePy ($ZEREBRO) has a 5% market share in the field of creative content and social media automation, with a total market value of $300 million. Its core advantage lies in the community-driven innovation ecosystem, which has enabled it to accumulate a loyal user base in application scenarios such as NFT, digital art, and social content automation. ZerePy lowers the development threshold of AI agents, allowing content creators and community operators to easily deploy intelligent agents to achieve automated content creation, social interaction, and community management, thereby increasing user engagement and content influence.
In terms of technical architecture, ZerePy is based on the Python ecosystem, providing a friendly development environment for AI/ML developers, and at the same time uses the modular Zerebro backend to achieve agent autonomy for social tasks. Its social platform integration function optimizes Twitter-like interactions, enabling agents to automatically complete tasks such as posting, replying, and retweeting, enhancing the automation capabilities of social media. In addition, ZerePy combines a lightweight architecture design, making it more suitable for the AI agent needs of individual creators and small communities without incurring high computing costs. ZerePy performs well in social interaction and creative content generation, but its scalability is mainly suitable for small-scale communities and is not very suitable for high-intensity enterprise-level tasks. At the same time, due to its relatively concentrated application scope, its applicability outside the creative field still needs further verification. For scenarios that require more complex creative outputs, ZerePy may require additional parameter tuning and model optimization to meet a wider range of market needs. With the development of the creative economy, ZerePy is expected to further expand its application scenarios in the future in the directions of NFT generation, personalized social agents, etc.
AI Launchpad not only provides a customized growth path for emerging projects, covering technical support, fund raising, marketing and cooperation opportunities with industry experts, but also helps projects quickly integrate into the global AI community through its extensive cooperation network.
11.Vvaifu:The firstAI Launchpad
vvaifu.fun is the first AI agent Launchpad based on the Solana chain, allowing users to create, manage and trade AI agents without any coding skills. The platform enables each AI agent to have its own token, thus forming a decentralized ecosystem. Users can not only co-own these agents, but also interact with AI-driven assets. The platform supports agents to interact autonomously on social media platforms such as Twitter, Discord and Telegram, and has on-chain wallet management functions, which greatly enhances its practicality in various application scenarios.
vvaifu.fun's business model is based on its unique token economic model. The platform's main token $VVAIFU is the first AI agent token launched on the Dasha platform. It has deflationary characteristics. Whenever an agent is created or a function is unlocked, a certain amount of $VVAIFU will be burned. In addition, the platform has designed a number of burning mechanisms to ensure the stability of the token value, including burning 750 $VVAIFU when the agent is created, consuming $VVAIFU and SOL fees when the function is unlocked, etc. Each launched agent will also allocate 0.90% of the new agent tokens to the community fund, or directly into the team treasury, thereby promoting community participation and ecological construction.
The platform's community participation mechanism enhances user interactivity and governance rights. Token holders can accumulate 0.90% of the supply at agent launch through the community wallet and vote on the use of these resources. vvaifu.fun also sets the platform transaction fee at 0.009 SOL, which provides sustainable economic support for the operation of the platform. Through these mechanisms, vvaifu.fun provides a comprehensive decentralized interactive platform for creators and users of AI agents, which not only promotes the development of creative projects, but also encourages the active participation of the global community.
12、Clanker：AI Reply Robot
Clanker is an AI reply robot based on Farcaster, designed for users to create and deploy memecoins and tokens. Through the platform, users can create their own tokens simply by interacting with Clanker. Users only need to tag @clanker on Farcaster, tell the robot what kind of token is needed, and provide information such as name, code, image, and supply. Clanker generates and provides tracking links within a minute, and eventually deploys the token to Uniswap v3, although there is no initial liquidity and users need to manually add liquidity to price the token.
The technical architecture behind Clanker works in conjunction with LLMs (such as Anthropic's Claude or ChatGPT) through Next.js middleware. When a user initiates a request on Farcaster, the message is forwarded to the LLM, which executes decision logic based on the provided context to determine the deployment operation of the token. This process demonstrates how Clanker uses AI technology to simplify the process of user generation and deployment of tokens, fully combining social platforms with blockchain technology to provide users with a convenient token creation experience.
As a platform, Clanker not only simplifies the creation process, but also deeply integrates with Uniswap v3, allowing users to deploy new tokens directly to decentralized exchanges. This process increases the speed of memecoins and token issuance, and also supports providing strategic value to the ecosystem through components such as Telegram robots, DEXs, and aggregators, thereby driving the growth of on-chain transactions. As the number of tokens increases, Clanker has participated in a significant increase in transaction volume, helping users take advantage of low transaction fees and fast confirmation times, and promoting the circulation of on-chain assets such as Solana and Base.
Technology-driven and infrastructure constitute the core of AI agent projects, ensuring efficient operation and supporting large-scale expansion through advanced programming languages and innovative algorithms. At the same time, high-performance blockchain platforms provide excellent transaction processing capabilities and multi-chain compatibility, enabling AI agents to interact seamlessly on different chains, promoting the continuous optimization and upgrading of technical foundations.
Payment and transaction infrastructure are key pillars for the development of the AI agent ecosystem. Stablecoin payment systems ensure transaction stability and liquidity, and improve the efficiency of interaction between AI agents and users. Decentralized autonomous trading systems achieve more efficient and secure automated transactions by eliminating human intermediaries. In addition, innovative reward and governance mechanisms such as "Proof of Contribution" and "Proof of Cooperation" promote AI agent collaboration and resource sharing, and ensure the long-term healthy development of the ecosystem through a sound governance system.
The necessity of AI Agent tokens is often questioned, mainly because they do not directly enhance the functionality of the agent or bring obvious advantages. Many people believe that AI Agent tokens are similar to tokens in Web3 games, which may not be of substantial help to the core functionality of the project. Therefore, some investors may ignore the actual value of these tokens by blindly following the AI craze, which brings high risks and even possible fraud. For such projects, some people believe that they attract uninformed investors by pretending to be legitimate, especially compared with meme coins, these tokens may promise too many unrealized functions.
If a project uses tokens as the primary driving force, it may lead to the sacrifice of core functions and experience, especially in non-gambling games and services. Tokens should be an additional element, not a dominant factor. Many successful projects have proved that truly effective applications should focus on user experience and create high-quality products, rather than relying solely on the economic incentive mechanism of tokens to attract users.
The integration of AI and DeFi will be an important trend in the future. It is expected that 80% of DeFi transactions will be completed by AI Agents. Promoters such as Modenetwork and Gizatech are also actively promoting this development. At the same time, the role of AI Agents in protocol governance will be further expanded, and may even trigger AI-driven governance attacks. In addition, security AI Agents are expected to play an important role in protecting protocols from attacks, similar to the protection functions provided by HypernativeLabs and FortaNetwork. As infrastructure continues to expand, the development of trusted execution environments (TEEs) and the core position of decentralized computing will enhance the resilience of AI Agents. In addition, the outbreak of the AI data market will also drive the growth of data payments between AIs, and projects such as Nevermined.io have laid the foundation for this.
Disclaimer
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