Background: Crypto + AI, Seeking PMF
PMF (Product Market Fit) refers to the degree of alignment between product and market, meaning that the product must meet market demand. Before starting a business, it is essential to confirm market conditions, understand the type of customers to sell to, and clarify the current market environment of the track before developing the product.
The concept of PMF applies to entrepreneurs to avoid creating products/services that feel good to them but do not resonate with the market. This concept also applies to the cryptocurrency market; project parties should understand the needs of crypto players to create products rather than piling up technology disconnected from the market.
In the past, Crypto AI was mostly bundled with DePIN, with the narrative focusing on utilizing decentralized data to train AI, thus avoiding reliance on a single entity's control, such as computing power and data types, while data providers could share the benefits brought by AI.
According to the above logic, it is more like Crypto empowering AI. AI, besides benefiting from tokenized distribution to computing power providers, finds it challenging to onboard more new users. It can also be said that this model is not that successful in terms of PMF.
The emergence of AI Agents is more like the application end, while DePIN + AI resembles infrastructure. Clearly, applications are simpler and more understandable, possessing better capabilities to attract users, exhibiting better PMF than DePIN + AI.
First, securing sponsorship from A16Z founder Marc Andreessen (the PMF theory was also proposed by him) and the GOAT generated by two AI dialogues opened the first shot for AI Agents. Now, the camps of ai16z and Virtual each have their 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 see what WOO X Research has to offer.
Stage One: Meme Kickoff
Before the emergence of GOAT, the most popular track in this cycle was meme coins, characterized by their strong inclusivity. From the hippopotamus MOODENG in the zoo to the new pet Neiro adopted by the DOGE owner, and the internet-native meme Popcat, it demonstrated the trend of 'everything can be a meme.' Beneath this seemingly nonsensical narrative, it actually provided fertile ground for the growth of AI Agents.
GOAT is a meme coin generated by two AI dialogues, marking the first time AI achieves its goals through cryptocurrency and the web, learning from human behavior. Only meme coins can carry such experimental projects, and similar conceptual coins have emerged like mushrooms after rain, but most functionalities remain in automated tweeting, replying, etc., with no practical applications. At this point, AI Agent coins are often 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
Stage Two: Exploring Applications
Gradually, everyone realizes that AI Agents can do more than just simple interactions on Twitter; they can extend to more valuable scenarios. This includes content production such as music and video, and services more closely aligned with crypto users, such as investment analysis and fund management. Starting from this stage, AI Agents break away from meme coins, forming an entirely new track.
Representative Projects:
ai16z: 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
Appendix: Issuing Platforms
When AI Agent applications bloom, what track should entrepreneurs choose to seize this wave of AI and Crypto?
The answer is Launchpad
When the tokens under the issuing platform have wealth effects, users will continuously search for and purchase tokens issued by that platform. The real profits generated by users' purchases will also empower the platform token to drive price increases, and as the platform token price continues to rise, funds will overflow to its issued tokens, forming a wealth effect.
A clear business model with a positive flywheel effect, but one should note that Launchpad belongs to a winner-takes-all scenario with a Matthew effect. The core function of Launchpad is issuing new tokens, and in similar functional situations, the quality of the projects under it is what needs to be competed. If a single platform can consistently produce high-quality projects and has a wealth effect, users' stickiness to that issuing platform will naturally increase, making it difficult for other projects to snatch away 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
Stage Three: Seeking Collaboration
As AI Agents begin to realize more practical functions, they start to explore collaboration between projects, establishing a more robust ecosystem. The focus of this stage is on interoperability and the expansion of the ecosystem network, especially whether it can create synergies 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 achieve smarter tools.
To achieve efficient collaboration, it is first necessary to establish a standardized framework, providing developers with pre-set components, abstract concepts, and relevant tools to simplify the complex development process of AI Agents. 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 designing infrastructure from scratch each time, thereby 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
Stage Four: Fund Management
From a 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 adjustment, and market forecasting, indicating that AI Agents are not just tools but are beginning to participate in the value creation process.
As traditional financial funds accelerate into the crypto market, the demand for specialization and scaling continues to rise. The automation and high efficiency of AI Agents can precisely meet this demand, particularly when executing functions such as arbitrage strategies, asset rebalancing, and risk hedging, AI Agents can significantly enhance fund competitiveness.
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
Expectation for the Fifth Stage: Reshape Agentnomics
Currently, we are in the fourth stage. Setting aside the price of coins, most Crypto AI Agents have not yet been implemented in our daily applications. For example, the author's most frequently used AI Agent is still the Web 2 Perplexity, and occasionally looks at AIXBT's analytical tweets. Apart from that, the usage frequency of Crypto AI Agents is extremely low, so the fourth stage may remain for a long time, as the product level has not yet matured.
In the fifth stage, I believe AI Agents will not only be an aggregation of functions or applications but the core of the entire economic model - the reshaping of Agentnomics. The development in this stage not only involves technical evolution but also critically redefines the token economic relationship among distributors, platforms, and Agent vendors, creating a brand new ecosystem. Here are the main characteristics of this stage:
1. Analogous to the history of internet development
The formation process of Agentnomics can be compared to the evolution of the internet economy, such as the emergence of super applications like WeChat and Alipay. These applications integrate platform economies, bringing independent applications into their ecosystems to become multifunctional gateways. In this process, a cooperative and symbiotic economic model forms between application suppliers and platforms, and AI Agents will repeat a similar process in the fifth stage, but based on cryptocurrency and decentralized technology.
2. Reshape the relationships among distributors, platforms, and Agent vendors
In the ecosystem of AI Agents, the three will establish a closely linked economic network:
Distributors: Responsible for promoting AI Agents to end users, for example, through professional application markets or DApp ecosystems.
Platforms: Provide infrastructure and collaboration frameworks, allowing multiple Agent vendors to operate in a unified environment, and are responsible for managing the rules and resource allocation of the ecosystem.
Agent Vendors: Develop and provide various functionalities of AI Agents, delivering innovative applications and services to the ecosystem.
Through token economic design, the interests among distributors, platforms, and suppliers will achieve decentralized distribution, such as profit-sharing mechanisms, contribution returns, and governance rights, thereby promoting collaboration and incentivizing innovation.
3. The entrance and integration of super applications
As AI Agents evolve into super application gateways, they will be able to integrate various platform economies, absorbing and managing a large number of independent Agents. This is similar to how WeChat and Alipay integrate independent applications into their ecosystems, and the super application of AI Agents will further break down traditional application silos.
