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, it is essential to confirm the market situation, understand the types of customers to sell to, and grasp the current market environment of the sector 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 resonate with the market. This concept is also relevant in the cryptocurrency market, where project teams should understand the needs of crypto players to create products, rather than piling up technology that is disconnected from the market.

In the past, Crypto AI was mostly bundled with DePIN, with the narrative focusing on using Crypto's decentralized data to train AI, thereby avoiding reliance on a single entity's control, such as computing power and data. Meanwhile, data providers could share the benefits brought by AI.

According to the above logic, it is more like Crypto empowering AI. Besides tokenizing benefits to distribute to computing power providers, it is difficult for AI to onboard more new users. One could say that this model is not very successful in terms of PMF.

The emergence of AI Agents is more like the application end, contrasting with DePIN + AI, which resembles infrastructure. Clearly, applications are simpler and easier to understand, and they have a better ability to attract users, achieving better PMF than DePIN + AI.

First, it received sponsorship from Marc Andreessen, the founder of A16Z (the PMF theory was also proposed by him), and the GOAT generated from a conversation between two AIs marked the first shot of AI Agents. Now, both ai16z and Virtual have their respective strengths and weaknesses. What is the development trajectory of AI Agents in the crypto space? What stage are they currently in? Where will they go in the future? Let's take a look with WOO X Research.

Stage One: Meme Kickoff

Before the emergence of GOAT, the hottest sector in this cycle was meme coins, characterized by strong inclusivity. From the hippo MOODENG from the zoo to DOGE's newly adopted Neiro, and the internet-native meme Popcat, it showcased 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 from a conversation between two AIs, marking the first time AI achieved its goals through cryptocurrency and the internet, learning from human behavior. Only meme coins could carry such a high experimental nature project. Meanwhile, similar conceptual coins sprang up like mushrooms after rain, but most functionalities remained limited to automatic posting and replying on Twitter, with no practical applications. At this time, AI Agent coins were 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, people realized that AI Agents could do more than simple interactions on Twitter; they could extend to more valuable scenarios. This includes content production such as music and video, as well as services more aligned with crypto users, like investment analysis and fund management. From this stage onward, AI Agents began to separate from meme coins, forming an entirely new sector.

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

Side Note: Issuing Platforms

As AI Agent applications flourish, what sector should entrepreneurs choose to seize the wave of AI and Crypto?

The answer is Launchpad.

When the tokens issued under the platform have a wealth effect, users will continuously seek and purchase tokens issued by that platform. The real profits generated from user purchases also empower the platform token to drive price increases. 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 it is important to note that Launchpad belongs to a winner-takes-all scenario with a Matthew effect. The core function of Launchpad is to issue new tokens, and in similar functional situations, the competition will be the quality of the projects under it. If a single platform can consistently produce high-quality projects and has a wealth effect, user stickiness to that issuing 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 begin to realize more practical functions, they start exploring collaboration between projects to build a more robust ecosystem. The focus of this stage is on interoperability and the expansion of the ecosystem, particularly whether they can create synergies with other crypto projects or protocols. For example, AI Agents might 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 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 play a more straightforward tool role, such as providing investment advice and generating reports. However, fund management requires a higher level of capability, including strategy design, dynamic adjustment, and market forecasting, marking that AI Agents are not just tools but are beginning to participate in the value creation process.

As traditional financial capital accelerates 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 when executing functions like 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

Anticipating Stage Five: Reshaping Agentnomics

Currently, we are in Stage Four. Setting aside token prices, most Crypto AI Agents have not yet been implemented in our daily applications. For example, the AI Agent I use most often is still the Web2 Perplexity, and I occasionally check AIXBT's analysis tweets. Aside from that, the usage frequency of Crypto AI Agents is extremely low, so Stage Four may linger for a while, as the product aspect is not yet mature.

I believe that in Stage Five, AI Agents will not just be a collection of functions or applications but will become the core of the entire economic model—reshaping Agentnomics. The development in this stage will involve not only technological evolution but also a critical redefinition of the token economic relationships between distributors, platforms, and Agent vendors, creating a new ecosystem. The following are the main characteristics 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 emergence of super applications like WeChat and Alipay. These applications integrate platform economies, bringing independent applications into their ecosystems, becoming multifunctional entry points. In this process, a collaborative and symbiotic economic model is formed between application providers and platforms, and AI Agents will replay a similar process in Stage Five, but based on cryptocurrency and decentralized technology.

2. Reshaping the Relationship Between Distributors, Platforms, and Agent Vendors

In the ecosystem of AI Agents, the three will establish a closely connected 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 managing the rules and resource allocation of the ecosystem.

• Agent Vendor: Develops and provides different functionalities of AI Agents, delivering innovative applications and services to the ecosystem.

Through token economic design, the interests between distributors, platforms, and vendors will achieve decentralized distribution, such as revenue-sharing mechanisms, contribution returns, and governance rights, thereby promoting collaboration and incentivizing innovation.

3. Super Application Entry and Integration

As AI Agents evolve into super application entry points, 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.

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