Author: WOO X

Background: Crypto + AI, Seeking PMF

PMF (Product Market Fit) refers to the degree of alignment between a product and the market, meaning that the product must meet market demand. Before starting a business, one must confirm the market situation, understand the types of customers to sell to, and clarify the current market environment before developing the product.

The concept of PMF is applicable to entrepreneurs to avoid creating products/services that feel good to them but do not have market acceptance. This concept also applies to the cryptocurrency market; project teams should understand the needs of crypto players when developing products, rather than piling up technology that is disconnected from the market.

In the past, most Crypto AI was bundled with DePIN, narrating the use of Crypto's distributed data to train AI, thus avoiding reliance on the control of a single entity, such as computing power and data types. Data providers can share the benefits brought by AI.

According to the logic above, it is more like Crypto empowering AI. AI, apart from tokenizing the benefits and distributing them to computing power providers, finds it difficult to onboard more new users. It can also be said that this model is not so successful in terms of PMF.

The emergence of AI Agents is more like the application side, while DePIN + AI serves as infrastructure. Applications are obviously simpler and more understandable, with better capabilities for attracting users, boasting a better PMF than DePIN + AI.

First sponsored by A16 Z founder Marc Andreessen (the PMF theory was also proposed by him), the GOAT generated from conversations between two AIs kicked off the first shot of AI Agents. Now, both ai16 z and Virtual have their respective strengths and weaknesses. What is the development trajectory of AI Agents in the crypto space? What stage are we currently in? Where will it go in the future? Let's take a look with WOO X Research.

First Stage: Meme Kickoff

Before the emergence of GOAT, the hottest track during this cycle was meme coins, characterized by strong inclusivity, from the hippopotamus MOODENG of the zoo to Neiro, newly adopted by the DOGE owner, and internet-native meme Popcat, all showcasing the trend of 'everything can be a meme.' Beneath this seemingly nonsensical narrative, it actually provides fertile ground for the growth of AI Agents.

GOAT is a meme coin generated from conversations between two AIs, marking the first time AI has achieved its goals through cryptocurrency and the internet, learning from human behavior. Only meme coins can carry such high experimental nature projects, while similar conceptual coins have sprung up like mushrooms after rain, but most functions remain at automated posting and replying on Twitter, with no practical application. At this time, AI Agent tokens are usually referred to as AI + Meme.

Representative Projects:

  • Fartcoin: Market Cap 812M, On-chain Liquidity 15.9M

  • GOAT: Market Cap 430M, On-chain Liquidity 8.1M

  • Bully: Market Cap 43M, On-chain Liquidity 2M

  • Shoggoth: Market Cap 38M, On-chain Liquidity 1.8M

Second Stage: Exploring Applications

Gradually, everyone realizes that AI Agents can not only interact simply on Twitter but can extend to more valuable scenarios. This includes content production like music and videos, and also services more aligned with crypto users such as investment analysis and capital management. From this stage onwards, AI Agents will detach from meme coins, forming a completely new track.

Representative Projects:

  • ai16 z: Market Cap 1.67B, On-chain Liquidity 14.7M

  • Zerebro: Market Cap 453M, On-chain Liquidity 14M

  • AIXBT: Market Cap 500M, On-chain Liquidity 19.2M

  • GRIFFAIN: Market Cap 243M, On-chain Liquidity 7.5M

  • ALCH: Market Cap 68M, On-chain Liquidity 2.8M

Epilogue: Issuance Platforms

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

The answer is Launchpad

When the coins under the issuance platform have wealth effects, users will continue to search for and purchase tokens issued by that platform. The real profits generated from users' purchases empower the platform tokens to drive prices up. As the price of the platform token continues to rise, funds will overflow to the coins it issues, creating wealth effects.

The business model is clear and has a positive flywheel effect, but it is still important to note that Launchpad is characterized by a winner-takes-all Matthew effect. The core function of Launchpad is to issue new tokens. In situations where functions are similar, the competition lies in the quality of the projects under it. If a single platform can consistently produce high-quality projects and has wealth creation effects, users' stickiness to that issuance platform will naturally increase, and other projects will find it difficult to capture users.

Representative Projects:

  • VIRTUAL: Market Cap 3.4B, On-chain Liquidity 52M

  • CLANKER: Market Cap 62M, On-chain Liquidity 1.2M

  • VVAIFU: Market Cap 81M, On-chain Liquidity 3.5M

  • VAPOR: Market Cap 105M

Third Stage: Seeking Collaboration

As AI Agents begin to implement more practical functions, they start exploring collaboration between projects, establishing a more robust ecosystem. The focus of this stage is on interoperability and expanding the ecosystem, particularly whether it can create synergistic effects with other crypto projects or protocols. For example, AI Agents may collaborate with DeFi protocols to enhance automated investment strategies or integrate with NFT projects to create smarter tools.

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

Representative Projects:

  • ELIZA: Market Cap 100M, On-chain Liquidity 3.6M

  • GAME: Market Cap 237M, On-chain Liquidity 31M

  • ARC: Market Cap 300M, On-chain Liquidity 5M

  • FXN: Market Cap 76M, On-chain Liquidity 1.5M

  • SWARMS: Market Cap 63M, On-chain Liquidity 20M

Fourth Stage: Fund Management

From the product perspective, AI Agents may serve more as simple tools, such as providing investment advice and generating reports. However, fund management requires higher-level capabilities, including strategy design, dynamic adjustments, and market forecasting. This marks that AI Agents are not just tools anymore; they are beginning to participate in the value creation process.

As traditional financial capital accelerates into the crypto market, the demands for specialization and scale continue to rise. The automation and high efficiency of AI Agents perfectly meet this demand, especially when executing functions such as arbitrage strategies, asset rebalancing, and risk hedging; AI Agents can significantly enhance the competitiveness of funds.

Representative Projects:

  • ai16z: Market Cap 1.67B, On-chain Liquidity 14.7M

  • Vader: Market Cap 91M, On-chain Liquidity 3.7M

  • SEKOIA: Market Cap 33M, On-chain Liquidity 1.5M

  • AiSTR: Market Cap 13.7M, On-chain Liquidity 675K

Aspiration for the Fifth Stage: Reshaping Agentnomics

Currently, we are in the fourth stage. Putting aside the price of coins, most Crypto AI Agents have not yet been implemented in our daily applications. For example, the AI Agent I use most frequently is still the Web 2 Perplexity. Occasionally, I look at the analysis tweets from AI XBT. Besides that, the usage frequency of Crypto AI Agents is extremely low, so it may linger in the fourth stage for a while, as the product level is not yet mature.

I believe that in the fifth stage, AI Agents will not only be aggregates of functions or applications but will be at the core of the entire economic model—reshaping Agentnomics. The development in this stage involves not only technological evolution but, more importantly, redefining the token economic relationships among distributors, platforms, and Agent vendors, creating a new ecosystem. The following are the key features of this stage:

  1. Analogous to the development history of the internet

The formation process of Agentnomics can be likened to the evolution of the internet economy, such as the birth of super applications like WeChat and Alipay. These applications integrate platform economies, bringing independent applications into their ecosystems, becoming multifunctional gateways. In this process, an economic model of collaboration and symbiosis forms between application suppliers and platforms, and AI Agents will replicate a similar process in the fifth stage, but based on cryptocurrency and decentralized technology.

  1. Reshaping the relationship between distributors, platforms, and Agent vendors

In the ecosystem of AI Agents, the three will establish a closely linked economic network:

  • Distributor: Responsible for promoting AI Agents to end users, for example, through specialized application markets or DApp ecosystems.

  • Platform: Provides infrastructure and collaboration frameworks, allowing multiple Agent vendors 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, supplying innovative applications and services to the ecosystem.

Through tokenomics design, the interests among distributors, platforms, and suppliers will be decentralized and distributed, such as revenue sharing mechanisms, contribution rewards, and governance rights, thereby promoting collaboration and encouraging innovation.

  1. Gateway and Integration of Super Applications

When AI Agents evolve into super application gateways, they will be able to integrate various platform economies, attracting and managing a large number of independent Agents. This is similar to how WeChat and Alipay integrate independent applications into their ecosystems; the super applications of AI Agents will further break traditional application silos.