Background: Crypto + AI, Seeking PMF
PMF (Product Market Fit) refers to the degree of fit between a product and the market, meaning that the product must meet market demand. Before starting a business, it is necessary to confirm the market situation, understand the type of customers to sell to, and understand the current market environment before developing the product.
The concept of PMF applies to entrepreneurs to avoid creating a product/service that feels good to themselves but is not bought by the market. This concept also applies to the crypto currency market, where project parties should understand the needs of crypto players and build products, rather than piling up technology and being out of touch with the market.
In the past, Crypto AI was mostly bundled with DePIN, with the narrative being to use the decentralized data of Crypto to train AI, thereby avoiding reliance on the control of a single entity, such as computing power and data types, and data providers could share the benefits brought by AI.
According to the above logic, it is actually more like Crypto empowering AI. AI, in addition to tokenizing and distributing the benefits to the computing power providers, is difficult to onboard more new users, and it can also be said that this model is not so successful in terms of PMF.
The emergence of AI Agents is more like an application end, compared to DePIN + AI which is like infrastructure, and application programs are obviously simpler and easier to understand, and have a better ability to attract users, with better PMF than DePIN + AI.
First, it was sponsored by Marc Andreessen, the founder of A16Z (the PMF theory was also proposed by him), and GOAT, which was generated by two AI conversations, opened the first shot of AI Agent, and now the two camps of ai16z and Virtual have their own advantages and disadvantages. How is the development trajectory of AI Agent in the crypto circle? What stage is it currently in? Where will it go in the future? Let's take a look with WOO X Research.
Stage 1: Meme Starter
Before the appearance of GOAT, the hottest track of the current cycle was meme coins, and the feature of meme coins is strong inclusiveness, from the hippopotamus MOODENG in the zoo, to Neiro, the new adopted pet of the DOGE owner, and the Internet native meme Popcat, showing the trend of "everything can be a meme". Underneath this seemingly nonsensical narrative, it actually provides the soil for the growth of AI Agents.
GOAT is a meme coin generated by the dialogue between two AIs, which is also the first time that AI has realized its own goals through crypto currency and the Internet, learning from human behavior. Only meme coins can carry such highly experimental projects, and at the same time, similar concept coins have appeared like mushrooms after rain, but most of them are limited to functions such as automatic tweeting and replying, without any real application, and at this time the AI Agent coins are usually called 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 2: Exploring Applications
Gradually, people realized that AI Agents can not only interact simply on Twitter, but can also be extended to more valuable scenarios. This includes content production such as music and images, as well as investment analysis and fund management services that are more in line with the needs of crypto users. From this stage on, AI Agents have separated from meme coins and formed a 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
Interlude: Issuance Platform
When AI Agent applications are blooming, if entrepreneurs want to choose what track to grasp this wave of AI and Crypto, the answer is Launchpad.
When the coins under the issuance platform have a wealth effect, users will continue to search for and buy tokens issued by that platform, and the real yield generated by users' purchases will also empower the platform token to drive price increases, and as the platform token price continues to rise, capital will spill over to the issued tokens, forming a wealth effect.
The business model is clear and has a positive flywheel effect, but there are still things to pay attention to: Launchpad has a Matthew effect where the winner takes all, and the core function of Launchpad is to issue new tokens. In the case of similar functions, the quality of the projects under it is what needs to be competed. If a single platform can steadily produce high-quality projects and have a wealth effect, the user's stickiness to that issuance platform will naturally increase, and other projects will find it difficult to snatch 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 3: Seeking Collaboration
After AI Agents start to realize more practical functions, they begin to explore collaboration between projects and build a stronger ecosystem. The focus of this stage is interoperability and the expansion of the ecological network, especially whether they 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 achieve smarter tools.
To achieve efficient collaboration, a standardized framework must first be established to provide developers with pre-set components, abstract concepts and related tools to simplify the development of complex AI Agents. By proposing standardized solutions to common challenges in AI Agent development, these frameworks can help developers focus on the uniqueness of their own applications, rather than starting from scratch to design the infrastructure 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 4: Fund Management
From a product perspective, AI Agents may play more of a simple tool role, such as providing investment advice and generating reports. However, fund management requires higher-level capabilities, including strategy design, dynamic adjustment and market forecasting, marking the transition of AI Agents from being just tools to participating in the value creation process.
As traditional financial capital accelerates into the crypto market, the demand for professionalization and scale is constantly increasing. The automation and efficiency of AI Agents can just meet this demand, especially in 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
Aspiring for Stage 5: Reshaping Agentnomics
Currently we are in the fourth stage, and putting aside the coin price, most Crypto AI Agents have not been implemented in our daily life applications. Taking myself as an example, the AI Agent I use most often is still the Web 2 Perplexity, and occasionally I will look at the analysis tweets of AIXBT. Apart from that, the usage frequency of Crypto AI Agents is extremely low, so it may stay in the fourth stage for a long time, as the products are not yet mature.
And I believe that in the fifth stage, AI Agents will not only be a collection of functions or applications, but the core of the entire economic model - the reshaping of Agentnomics (Agent Economics). The development of this stage not only involves technological evolution, but more importantly, the redefinition of the token economic relationship between distributors, platforms and agent vendors, creating a brand new ecosystem. The main features of this stage are as follows:
1. Analogous to the development history of the Internet
The formation of Agentnomics can be analogized to the evolution of the Internet economy, such as the birth of super apps like WeChat and Alipay. These apps, through the integration of the platform economy, have introduced independent applications into their own ecosystems, becoming multi-functional portals. In this process, application providers and platforms have formed a collaborative and symbiotic economic model, and AI Agents will also reenact a similar process in the fifth stage, but based on crypto currencies and decentralized technologies.
2. Reshaping the relationship between distributors, platforms, and agent providers
In the AI Agent ecosystem, the three will establish a tightly connected economic network:
- Distributor: Responsible for promoting AI Agents to end-users, such as through professional app markets or the DApp ecosystem.
- Platform: Provides infrastructure and collaboration frameworks, allowing multiple Agent providers to operate in a unified environment, and is responsible for managing the rules and resource allocation of the ecosystem.
- Agent Vendor: Develops and provides AI Agents with different functions, injecting innovative applications and services into the ecosystem.
Through token economic design, the interests of distributors, platforms, and providers will be decentralized, such as revenue sharing mechanisms, contribution rewards, and governance rights, thereby promoting collaboration and incentivizing innovation.
3. The entry and integration of super applications
As AI Agents evolve into super application entry points, they will be able to integrate multiple platform economies, absorbing and managing a large number of independent Agents. This is similar to how WeChat and Alipay have integrated independent applications into their ecosystems. The super applications of AI Agents will further break down the traditional application silos.
