Article Reprinted From: WOO
Background: Crypto + AI, Seeking PMF
PMF (Product Market Fit) refers to the degree of matching between product and market, meaning that products must meet market demands. Before entrepreneurship, one should confirm market conditions, understand what types of customers to sell to, and grasp the current market environment of the track before proceeding with product development.
The concept of PMF is applicable to entrepreneurs to avoid creating products/services that feel good to themselves but do not resonate with the market. This concept is also applicable in the cryptocurrency market, where project parties should understand the needs of crypto players to create products rather than just piling up technology disconnected from the market.
In the past, most Crypto AI was bundled with DePIN, with narratives centered around using decentralized data from Crypto to train AI, thus avoiding reliance on the control of a single entity, such as computing power and data types, while data providers could share the benefits brought by AI.
According to the logic above, it is more like Crypto empowering AI. AI, aside from benefiting from tokenized distributions to computing power providers, finds it difficult to onboard new users. It can also be said that this model is not so successful in terms of PMF.
The emergence of AI Agents appears more as an application side, compared to DePIN + AI which is more like infrastructure. Clearly, applications are simpler and have a better ability to attract users, achieving a better PMF than DePIN + AI.
First backed by A16Z founder Marc Andreessen (the PMF theory was also proposed by him), GOAT, generated from the conversations of two AIs, fired the first shot of AI Agents. Now, ai16z and Virtual each have their strengths and weaknesses in the crypto circle. What is the development trajectory of AI Agents? What phase are we currently in? Where will it go in the future? Let WOO X Research show you.
Phase One: Meme Kickoff
Before the emergence of GOAT, the hottest track in this cycle was meme coins, characterized by strong inclusivity—from the hippopotamus MOODENG in the zoo to DOGE's newly adopted Neiro, and the internet-native meme Popcat—all showcasing the trend of 'everything can be a meme.' Beneath this seemingly absurd narrative lies the fertile ground for AI Agents to grow.
GOAT is a meme coin generated from the conversations of 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 experimental projects, while similar conceptual coins spring up like mushrooms after rain, but most functions remain at automated tweeting and replies, lacking practical applications. At this time, AI Agent coins are usually referred to as AI + Meme.
Representative Project:
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
Phase Two: Exploring Applications
Gradually, everyone realizes that AI Agents can not only engage in simple interactions on Twitter but can extend to more valuable scenarios. This includes content production such as music and videos, as well as services like investment analysis and fund management that are more relevant to crypto users. From this phase onwards, AI Agents separate from meme coins, forming an entirely new track.
Representative Project:
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
Side Story: Issuing Platform
As AI Agent applications flourish, what tracks should entrepreneurs choose to seize this wave of AI and Crypto?
The answer is Launchpad
When the tokens under an issuing platform have wealth effects, users will continuously seek and purchase tokens issued by that platform. The real gains generated by user purchases will empower the platform’s token to drive the price up. As the platform token price continues to rise, funds will overflow to the tokens issued under it, creating a wealth effect.
The business model is clear and has a positive flywheel effect, but attention must be paid to the following: Launchpads belong to a winner-takes-all scenario with a Matthew effect. The core function of Launchpads is to issue new tokens. In similar functional situations, the competition is about the quality of the projects under it. If a single platform can consistently produce high-quality projects and has wealth-creating effects, user loyalty to the issuing platform will naturally increase, and other projects will find it difficult to steal users.
Representative Project:
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
Phase Three: Seeking Collaboration
As AI Agents begin to realize more practical functions, they start exploring collaboration between projects, establishing a more robust ecosystem. The focus of this phase is on interoperability and the expansion of the ecosystem, particularly whether cooperation can be achieved with other crypto projects or protocols. For example, AI Agents might cooperate with DeFi protocols to enhance automated investment strategies or integrate with NFT projects to create smarter tools.
To achieve efficient collaboration, it is essential to establish a standardized framework that provides developers with preset components, abstract concepts, and related 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 on the uniqueness of their applications rather than starting from scratch each time, thereby avoiding the problem of reinventing the wheel.
Representative Project:
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
Phase 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 adjustments, and market forecasting, marking AI Agents not just as tools, but as participants in the value creation process.
As traditional financial capital accelerates into the crypto market, the demand for specialization and scaling continues to rise. The automation and high efficiency of AI Agents can 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 Project:
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
Looking forward to Phase Five: Reshaping Agentnomics
Currently, we are in Phase Four. Setting aside the price of coins, most current 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 the analysis tweets from AIXBT. Apart from that, the usage frequency of Crypto AI Agents is extremely low, so Phase Four may linger for a while, as the product level is not yet mature.
The author believes that in Phase Five, AI Agents will not just be a collection of functions or applications but the core of an entire economic model—Agentnomics. The development in this phase involves not only technological evolution but also, more critically, redefining the token economic relationships between distributors, platforms, and Agent vendors, creating a new ecosystem. Below are the main characteristics of this phase:
1. Analogous to the development history of the internet
The formation process of Agentnomics can be likened to the evolution of internet economy, such as the birth of super applications like WeChat and Alipay. These applications integrate platform economies by bringing independent applications into their ecosystem, becoming multifunctional gateways. In this process, a collaborative and symbiotic economic model is formed between application providers and platforms, and AI Agents will reenact a similar process in Phase Five, but based on cryptocurrency and decentralized technologies.
2. Reshape 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, such as 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: Develops and provides different functions of AI Agents, delivering innovative applications and services to the ecosystem.
Through token economic design, the interests between 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. Gateway and Integration of Super Applications
When 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; the super application of AI Agents will further break traditional application silos.
