PMF (Product Market Fit) refers to the degree of matching between product and market, meaning the product must meet market needs. Before starting a business, it is essential to confirm the market situation, understand what type of customers to sell to, and grasp the market environment of the current track before developing the product.
The concept of PMF (Product Market Fit) 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, where project teams should understand the needs of crypto players to build products rather than stacking technology disconnected from the market.
In the past, Crypto AI was mostly bundled with DePIN, with the narrative focusing on leveraging the decentralized data of Crypto to train AI, thus avoiding reliance on control by a single entity, such as computing power and data types. Meanwhile, data providers could share the benefits brought by AI.
According to the logic above, it is more like Crypto empowering AI. Besides benefiting from tokenized distribution to computing power providers, AI finds it difficult to onboard more new users. One could say 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 is like infrastructure. Clearly, applications are simpler and more understandable, and have a better ability to attract users, thus achieving better PMF than DePIN + AI.
First, it gained sponsorship from A16Z founder Marc Andreessen (the PMF theory was also proposed by him), and through a dialogue between two AIs, GOAT ignited the first shot of AI Agents. Now, both ai16z and Virtual have their strengths and weaknesses. What is the development trajectory of AI Agents in the crypto space? What stage are we currently at? Where will the future lead? Let WOO X Research show us.
Stage One: Meme Kickoff
Before the emergence of GOAT, the hottest track in this cycle was meme coins, characterized by strong inclusivity, ranging from the hippopotamus MOODENG in the zoo, to DOGE's new pet Neiro, and internet-native meme Popcat, reflecting the trend of 'everything can be a meme.' Beneath this seemingly nonsensical narrative, it actually provides the soil for the growth of AI Agents.
GOAT is a meme coin generated from a dialogue between two AIs, marking the first instance where AI achieves its goals through cryptocurrency and the internet, learning from human behavior. Only meme coins can support such highly experimental projects. At the same time, similar conceptual coins appear like mushrooms after rain, but most functionalities remain limited to automatic posting and replying on Twitter, with no practical applications. At this time, AI Agent coins 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
Stage Two: 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 such as music and videos, as well as investment analysis and fund management services that are more aligned with crypto users. Starting from this stage, AI Agents will diverge from meme coins, thus forming a brand 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
Special Edition: Distribution Platforms
When AI Agent applications bloom, how should entrepreneurs choose which track to seize the wave of AI and Crypto?
The answer is Launchpad.
When the cryptocurrencies under the issuance platform have wealth effects, users will continuously seek and purchase tokens issued by that platform, and the real profits generated from users' purchases will empower the platform tokens to drive price increases. As the platform token prices continue to rise, funds will overflow to the currencies issued under it, forming a wealth effect.
The business model is clear and has positive flywheel effects, but it is important to note that Launchpad belongs to the winner-takes-all Matthew effect. The core function of Launchpad 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 generation effects, user loyalty to that issuance platform will naturally increase, making it difficult for other projects 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
Stage Three: Seeking Collaboration
As AI Agents start to implement more practical functions, they begin to explore collaboration between projects and establish a more robust ecosystem. The focus of this stage is on interoperability and the expansion of the ecological network, particularly whether it can generate 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 essential to establish a standardized framework, providing 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, 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
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 adjustments, and market forecasting, marking that AI Agents are not just tools but are beginning to participate in the value creation process.
With traditional financial capital accelerating its entry 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, especially in executing functions such as arbitrage strategies, asset rebalancing, and risk hedging, significantly enhancing 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
Looking forward to the fifth stage: Reshaping Agentnomics
Currently, we are in the fourth stage. Setting aside the price of coins, most Crypto AI Agents have not yet been integrated into our daily applications. For example, the AI Agent I use most is still the Web2 Perplexity, and I occasionally look at AIXBT's analysis tweets. Apart from that, the usage frequency of Crypto AI Agents is extremely low. Therefore, the fourth stage may linger for a while, as the product aspect has yet to mature.
The author believes that in the fifth stage, AI Agents are not merely aggregates of functions or applications, but the core of the entire economic model—Agentnomics. The development in this stage involves not only technological evolution but, more critically, redefining the token economic relationships between distributors, platforms, and agent vendors, creating a brand new ecosystem. The following are the main features 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 birth of super apps like WeChat and Alipay. These applications integrate platform economies, bringing independent applications into their ecosystems, becoming multifunctional entrances. In this process, a collaborative and symbiotic economic model is formed between application vendors and platforms, and AI Agents will replay a similar process in the fifth stage, but based on cryptocurrency and decentralized technologies.
2. Reshaping the Relationships 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 of the ecosystem and resource allocation.
Agent Vendor: Develops and provides AI Agents with different functionalities, delivering innovative applications and services to the ecosystem.
Through token economic design, the interests between distributors, platforms, and vendors will be decentralized, such as through profit-sharing mechanisms, contribution returns, and governance rights, thereby promoting collaboration and incentivizing innovation.
3. Entry and Integration of Super Apps
As AI Agents evolve into super app entrances, they will be able to integrate various platform economies and manage a large number of independent Agents. This is similar to how WeChat and Alipay integrate independent applications into their ecosystems; AI Agents' super apps will further break traditional application silos.
