Analogous to the Internet, what stage has the AI Agent in the crypto market reached now?

Background: Crypto + AI, looking for PMF

PMF (Product Market Fit) refers to product market fit, which means that the product must meet market demand. Before starting a business, you should first confirm the market situation, understand what type of customers you want to sell to, and understand the market environment of the current track before developing the product.

The concept of PMF applies to entrepreneurs, so as to avoid creating products/services that they feel good about but the market does not buy. The concept is also applicable to the cryptocurrency market. Project parties should understand the needs of cryptocurrency players to create products, rather than piling up technology that is out of touch with the market.

In the past, Crypto AI was mostly bundled with DePIN. The narrative was to use Crypto's decentralized data to train AI, thereby avoiding reliance on the control of a single entity, such as computing power, data, etc., while data providers can share the benefits brought by AI.

According to the above logic, it is more like Crypto empowering AI. In addition to distributing the benefits to computing power providers in token form, AI finds it difficult to onboard more new users. It can also be said that this model is not so successful in PMF.

The emergence of AI Agent is more like an application end, compared to DePIN + AI which is like infrastructure. Obviously, the application is simpler and easier to understand, and has a better ability to attract users, and has a better PMF than DePIN + AI.

First, GOAT, sponsored by A16Z founder Marc Andreessen (who also proposed the PMF theory), was created by a conversation between two AIs, which fired the first shot of AI Agent. Now, both ai16z and Virtual have their own advantages and disadvantages. What is the development trajectory of AI Agent in the cryptocurrency circle? What stage is it in now? Where will it go in the future? Let WOO X Research show you.

Phase 1: Meme Starts

Before the emergence of GOAT, the hottest track in this cycle was meme coins, and one of the characteristics of meme coins is their strong inclusiveness. From the zoo's hippo MOODENG, to the newly adopted Neiro of DOGE's owner, to the Internet's native meme Popcat, etc., they show the trend of "everything can be a meme". Such seemingly nonsensical narratives actually provide a fertile soil for the growth of AI Agents.

GOAT is a meme coin generated by a conversation between two AIs. This is the first time that AI has achieved its own goals through cryptocurrency and the Internet and learned from human behavior. Only meme coins can carry such a highly experimental project. At the same time, similar concept coins have sprung up, but most of their functions remain at automatic Twitter posting and replying, etc., without practical applications. At this time, AI Agent coins are usually called AI + Meme.

Representative projects:

• Fartcoin: Market capitalization 812M, on-chain liquidity 15.9M
• GOAT: Market value 430M, on-chain liquidity 8.1M
• Bully: Market cap 43M, on-chain liquidity 2M
• Shoggoth: Market capitalization 38M, on-chain liquidity 1.8M

Phase 2: Exploring Applications

Gradually, people realized that AI Agents can not only interact with each other on Twitter, but also extend to more valuable scenarios. This includes content production such as music and video, as well as investment analysis, fund management and other services that are more suitable for cryptocurrency users. From this stage, AI Agents separated from meme coins, forming a new track.

Representative projects:

• ai16z: Market value 1.67B, on-chain liquidity 14.7M
• Zerebro: Market capitalization 453M, on-chain liquidity 14M
• AIXBT: Market value 500M, on-chain liquidity 19.2M
• GRIFFAIN: Market capitalization 243M, on-chain liquidity 7.5M
• ALCH: Market value 68M, on-chain liquidity 2.8M

Extra: Distribution Platform

As AI Agent applications flourish, which track should entrepreneurs choose to seize this wave of AI and Crypto?

The Answer is Launchpad

When the currency under the issuing platform has a wealth effect, users will continue to look for and purchase tokens issued by the platform. The real income generated by the users' purchases will also enable the platform currency to drive up the price. As the price of the platform currency continues to rise, funds will spill over to the currency issued under it, forming a wealth effect.

The business model is clear and has a positive flywheel effect, but it should be noted that Launchpad is a winner-takes-all Matthew effect. The core function of Launchpad is to issue new tokens. Under similar functions, what needs to be competed is the quality of the projects under it. If a single platform can stably produce high-quality projects and has a wealth-creating effect, the user's stickiness to the issuance platform will naturally increase, and it will be difficult for other projects to snatch users.

Representative projects:

• VIRTUAL: Market value 3.4B, on-chain liquidity 52M
• CLANKER: Market value 62M, on-chain liquidity 1.2M
• VVAIFU: Market value 81M, on-chain liquidity 3.5M
• VAPOR: Market value 105M

Phase 3: Seeking collaboration

After AI Agent begins to realize more practical functions, it will begin to explore collaboration between projects and build a stronger ecosystem. The focus of this stage is on interoperability and the expansion of the ecological network, especially whether it can generate synergies with other crypto projects or protocols. For example, AI Agent may cooperate with DeFi protocols to improve automated investment strategies, or integrate with NFT projects to achieve smarter tools.

To achieve efficient collaboration, it is necessary to first establish a standardized framework to provide developers with preset components, abstract concepts, and related tools to simplify the development process of complex AI agents. By proposing standardized solutions to common challenges in AI agent development, these frameworks can help developers focus on the uniqueness of their respective applications, rather than designing the infrastructure from scratch every time, thus avoiding the problem of reinventing the wheel.

Representative projects:

• ELIZA: Market value 100M, on-chain liquidity 3.6M
• GAME: Market value 237M, on-chain liquidity 31M
• ARC: Market value 300M, on-chain liquidity 5M
• FXN: Market capitalization 76M, on-chain liquidity 1.5M
• SWARMS: Market value 63M, on-chain liquidity 20M

Phase 4: Fund Management

From a product perspective, AI Agents may serve as simple tools, such as providing investment advice and generating reports. However, fund management requires higher-level capabilities, including strategy design, dynamic adjustment, and market forecasting, which means that AI Agents are not just tools, but are beginning to participate in the process of value creation.

As traditional financial funds accelerate their entry into the crypto market, the demand for specialization and scale continues to increase. The automation and high efficiency of AI Agent can just make up for this demand, especially when executing functions such as arbitrage strategies, asset rebalancing and risk hedging, AI Agent can significantly improve the competitiveness of funds.

Representative projects:

• ai16z: Market value 1.67B, on-chain liquidity 14.7M
• Vader: Market value 91M, on-chain liquidity 3.7M
• SEKOIA: Market capitalization 33M, on-chain liquidity 1.5M
• AiSTR: Market value 13.7M, on-chain liquidity 675K

Looking forward to the fifth stage: Reshaping Agentnomics

We are currently in the fourth stage. Putting aside the price of the currency, most of the current Crypto AI Agents have not been implemented in our daily life applications. Take me as an example. The most commonly used AI Agent is still Web 2's Perplexity. I occasionally read AIXBT's analysis tweets. Apart from that, the frequency of use of Crypto AI Agents is extremely low. Therefore, it may stay in the fourth stage for a long time and is not yet mature at the product level.

The author believes that in the fifth stage, AI Agent is not just an aggregate of functions or applications, but a reshaping of the core of the entire economic model - Agentnomics. The development of this stage not only involves technological evolution, but more importantly, it redefines the token economic relationship between distributors, platforms, and agent vendors to create a new ecosystem. The following are the main features of this stage:

1. Analogy to the development history of the Internet

The formation process of Agentnomics can be compared to the evolution of the Internet economy, such as the birth of super applications such as WeChat and Alipay. These applications integrate the platform economy and introduce independent applications into their own ecosystem, becoming a multi-functional portal. In this process, an economic model of collaboration and symbiosis is formed between application providers and platforms, and AI Agent will repeat a similar process in the fifth stage, but based on cryptocurrency and decentralized technology.

2. Reshape the relationship between distributors, platforms and agent suppliers

In the AI Agent ecosystem, the three will establish a closely connected economic network:

• Distributor: Responsible for promoting AI Agent to end users, such as through professional application markets or DApp ecosystems.
• Platform: Provides infrastructure and collaboration framework, allowing multiple Agent providers to operate in a unified environment, and is responsible for managing the rules and resource allocation of the ecosystem.
• Agent Vendor: Develop and provide AI Agents with different functions to deliver innovative applications and services to the ecosystem.

Through token economic design, the interests between distributors, platforms and suppliers will be decentralized, such as profit-sharing mechanisms, contribution rewards and governance rights, thereby promoting collaboration and stimulating innovation.

3. Super App Portal and Integration

When AI Agent evolves into a super application portal, it will be able to integrate multiple platform economies and absorb and manage a large number of independent agents. This is similar to how WeChat and Alipay integrate independent applications into their ecosystems. AI Agent's super application will further break the traditional application island.