Original Title: (WOO X Research: What Stage Has AI Agent Development Reached? How Will It Progress Next?)
Original Source: WOO X Research
Background: Crypto + AI, Seeking PMF
PMF (Product Market Fit) refers to the degree of product-market match, meaning that products must meet market demands. Before starting a startup, one should confirm the market situation, understand what type of customers to sell to, and grasp the current market environment before developing products.
The concept of PMF 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; project teams should understand the needs of crypto players to build 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, thus avoiding reliance on a single entity's control, such as computing power and data types, while data providers could share the benefits brought by AI.
According to the above logic, it seems more like Crypto empowering AI. Besides benefiting tokenized distributions to computing power providers, it is difficult to onboard more new users, suggesting that this model may not be very successful in PMF.
The emergence of AI Agents seems more like an application end, while DePIN + AI resembles infrastructure. Clearly, applications are simpler and more understandable, and have a better capacity to attract users, providing a better PMF than DePIN + AI.
First, obtaining sponsorship from A16Z founder Marc Andreessen (the PMF theory was also proposed by him), the GOAT generated from a conversation between two AIs sparked the first shot of AI Agents. Now, both ai16z and Virtual have their respective strengths and weaknesses. How has the development trajectory of AI Agents in the crypto space evolved? What stage are they currently in? Where will they go in the future? Let's take a look at it with WOO X Research.
First Stage: Meme Starting Point
Before the emergence of GOAT, the hottest track in this cycle was meme coins, characterized by their strong inclusivity. From the hippopotamus MOODENG from the zoo to the newly adopted Neiro by the DOGE owner, and the internet-native meme Popcat, they showcase the trend of 'everything can be a meme', which, beneath this seemingly nonsensical narrative, also provides fertile ground for the growth of AI Agents.
GOAT is a meme coin generated from a conversation 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 a high experimental nature project, and at the same time, similar conceptual tokens have sprung up like mushrooms after rain, but most functionalities remain at automatic posting and replying on Twitter, with no practical applications. At this time, 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, people realize that AI Agents can extend beyond simple interactions on Twitter 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 the needs of crypto users. From this stage onward, AI Agents diverge from meme coins, creating 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
(BlockBeats Note: Recently, the market has been volatile, and the cryptocurrencies mentioned in this article have experienced varying degrees of rise and fall, so the data in the article may differ from current data. This article is for reference only and does not constitute investment advice.)
Epilogue: Issuing Platforms
When AI Agent applications flourish, what track should entrepreneurs choose to seize this wave of AI and Crypto?
The answer is Launchpad
When the cryptocurrencies issued by the platform have wealth effects, users will continue to seek and purchase tokens issued by that platform, and the real profits generated by users' purchases will empower the platform tokens to drive up prices. As the platform token prices continue to rise, funds will overflow into the tokens issued under it, forming wealth effects.
A clear business model with positive flywheel effects, but still worth noting: Launchpad belongs to the winner-takes-all phenomenon of the Matthew effect. The core function of Launchpad is to issue new tokens, and in similar functional situations, the quality of projects under the platform is what matters. If a single platform can consistently produce high-quality projects and has a wealth creation effect, users' 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
Third Stage: Seeking Collaboration
As AI Agents begin to realize more practical functions, they start exploring collaboration between projects to establish a more robust ecosystem. The focus of this stage is on interoperability and the expansion of the ecosystem network, especially whether synergistic effects can be generated 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 implement smarter tools.
To achieve efficient collaboration, it is first necessary to establish a standardized framework that provides developers with pre-set 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 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
Fourth Stage: Fund Management
From the 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, indicating that AI Agents are not just tools but are beginning to participate in the value creation process.
As traditional financial capital accelerates into the crypto market, the demand for specialization and scalability continues to rise. The automation and high efficiency of AI Agents can effectively meet this demand, especially when 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
Anticipating the Fifth Stage: Reshaping Agentnomics
Currently, we are in the fourth stage. Aside from the price of coins, most Crypto AI Agents have not yet been implemented in our daily applications. Taking myself as an example, the most commonly used AI Agent remains the Web 2 Perplexity, and I occasionally check AIXBT's analytical tweets. Besides that, the usage frequency of Crypto AI Agents is extremely low, so it may remain in the fourth stage for a while, as the product level has not matured.
I believe that in the fifth stage, AI Agents will not just be an aggregation of functionalities or applications but will be at the core of the entire economic model—redefining Agentnomics. The development in this stage involves not only technological evolution but also a crucial 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. Drawing parallels with the history of internet development
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 ecosystem to become multifunctional entry points. In this process, a collaborative and symbiotic economic model forms between application providers and platforms, and AI Agents will replay a similar process in the fifth stage, 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 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 framework, 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 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 points and integration
When AI Agents evolve into super application entry points, they will integrate multiple platform economies and manage 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 down traditional application silos.
