Author: WOO X
Background: Crypto + AI, Seeking PMF
PMF (Product Market Fit) refers to the degree of product-market match, meaning that products must meet market demand. Before starting a venture, one should confirm market conditions, understand what type of customers to sell to, and clarify the current market environment of the track before proceeding with product development.
The concept of PMF applies to entrepreneurs, to avoid creating products/services that feel good to them but the market does not accept. This concept also applies to the cryptocurrency market, where project teams should understand the demands of cryptocurrency players to create products, rather than piling up technology that is disconnected from the market.
In the past, Crypto AI has mostly been bundled with DePIN, narrating the use of decentralized data to train AI, thus avoiding reliance on a single entity's control, such as computing power, data types, etc. Data providers can share the benefits brought by AI.
According to the logic above, it is more like Crypto empowering AI. Besides benefiting from the tokenized distribution to computing power providers, it is difficult for AI to onboard more new users. One could also say 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 is more like infrastructure. Clearly, applications are simpler and have better capabilities to attract users, with better PMF than DePIN + AI.
First sponsored by A16Z founder Marc Andreessen (who also proposed the PMF theory), GOAT was generated from a dialogue between two AIs, marking the first shot of AI Agent. Now, ai16z and Virtual have their respective strengths and weaknesses. What is the development trajectory of AI Agents in the cryptocurrency circle? What stage are they currently in? Where will they go in the future? Let's see what WOO X Research has to say.
First Stage: Meme Start
Before the emergence of GOAT, the hottest track in this cycle was meme coins, characterized by strong inclusivity. From the hippopotamus MOODENG from the zoo to the new pet of DOGE's owner, Neiro, and the internet-native meme Popcat, they showed the trend of 'everything can be a meme.' Beneath this seemingly nonsensical narrative, it also provided soil for the growth of AI Agents.
GOAT is a meme coin generated from a dialogue between two AIs. This is also 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 concept coins emerge like mushrooms after rain, but most of their functionalities remain at automatic tweeting, replying, etc., with no practical applications. At this point, AI Agent tokens 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
Second Stage: 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 in line with cryptocurrency users. From this stage on, AI Agents will separate from meme coins, thereby forming a completely new track.
Representative Projects:
ai16 z: 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
Epilogue: Issuing Platform
When AI Agent applications blossom, what track should entrepreneurs choose to seize the wave of AI and Crypto?
The answer is Launchpad
When the coins issued by a platform have wealth effects, users will continue to seek and purchase tokens issued by that platform. The real profits generated by users' purchases will empower the platform token to drive up prices. 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 lies in the quality of the projects under it. If a single platform can consistently produce high-quality projects and has a wealth creation effect, user adhesion to that issuing platform will naturally increase, making it difficult for other projects to snatch users away.
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
Third Stage: Seeking Collaboration
As AI Agents begin to realize more practical functions, they will start exploring collaboration between projects to establish a more powerful ecosystem. The focus of this stage is on interoperability and the expansion of the ecosystem, particularly whether it can generate synergies with other cryptocurrency 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, standardized frameworks must first be established to provide developers with preset 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 efforts on the uniqueness of their respective applications rather than designing the infrastructure 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
Fourth Stage: Fund Management
From a product perspective, AI Agents may act 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. This marks that AI Agents are not just tools but are beginning to participate in the value creation process.
As traditional financial capital accelerates into the cryptocurrency market, the demand for specialization and scalability is continuously increasing. The automation and high efficiency of AI Agents can precisely 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 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
Expecting the fifth stage: Restructuring Agentnomics
Currently, we are in the fourth stage. Setting aside the price of cryptocurrencies, 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 Web 2 Perplexity. Occasionally, I look at the analysis tweets from AI XBT. Other than that, the usage frequency of Crypto AI Agents is extremely low, so it may linger in the fourth stage for a while as the product level is not yet mature.
In my opinion, in the fifth stage, AI Agents are not just a collection of functions or applications but are the core of the entire economic model—the reshaping of Agent nomics. The development in this stage involves not only technological evolution but also the crucial redefinition of token economic relations between distributors, platforms, and Agent vendors, creating a new ecosystem. The following are the main characteristics of this stage:
Analogous to the history of internet development
The formation process of Agent nomics can be compared to the evolution of the internet economy, such as the birth of super applications like WeChat and Alipay. These applications integrate platform economies and bring independent applications into their own ecosystem, becoming multifunctional gateways. In this process, a collaborative and symbiotic economic model is formed between application providers and platforms, and AI Agents will replicate a similar process in the fifth stage, but based on cryptocurrency and decentralized technologies.
Restructuring the relationship between distributors, platforms, and Agent providers
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 professional application markets or DApp ecosystems.
Platform: Provides infrastructure and collaborative frameworks, allowing multiple Agent vendors to operate in a unified environment and responsible for 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 rewards, and governance rights, thereby promoting collaboration and incentivizing innovation.
Gateway and integration of super applications
When AI Agents evolve into super application gateways, 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 ecosystem; the super application of AI Agents will further break down traditional application silos.
