Original Title: (WOO X Research: What stage is AI Agent development currently in? What will the next steps be?)
Original source: WOO X Research
Background: Crypto + AI, seeking PMF
PMF (Product Market Fit) refers to the degree of product-market match, meaning that the product must meet market demand. Before starting a business, it is essential to 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 but do not sell in the market, and this concept also applies to the cryptocurrency market. Project teams should understand the needs of cryptocurrency players to create products, rather than piling up technology disconnected from the market.
In the past, most Crypto AI was bundled with DePIN, narrating the use of Crypto's decentralized data to train AI, thus avoiding reliance on a single entity's control, such as computing power, data, etc., while data providers could share the benefits brought by AI.
According to the above logic, it is more like Crypto empowering AI. Besides benefiting tokenized distributions to computing power providers, it is difficult for AI to onboard more new users. It can also be said that this model is not so successful in terms of PMF.
The emergence of AI Agents is more like the application end, compared to DePIN + AI, which is more like infrastructure. Clearly, applications are simpler and easier to understand, and they possess a better ability to attract users, with better PMF than DePIN + AI.
First sponsored by A16Z founder Marc Andreessen (the PMF theory was also proposed by him), the GOAT generated from a conversation between two AIs started 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 in? Where will we go in the future? Let WOO X Research show us.
First Stage: Meme Kickoff
Before the emergence of GOAT, the hottest track of this cycle was meme coins, characterized by strong inclusivity, from the hippo MOODENG of the zoo to the newly adopted Neiro by the DOGE owner, and the internet-native meme Popcat, all demonstrating the trend of 'everything can be a meme'. Beneath this seemingly nonsensical narrative lies the fertile soil for AI Agents to grow.
GOAT is a meme coin generated from a conversation between two AIs, marking 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, and simultaneously, similar concept coins appear like mushrooms after rain, but most functionalities remain at automatic tweeting, replying, etc., with no practical applications. At this time, AI Agent coins are usually referred to as AI + Meme.
Representative Project:
· 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 do more than just simple interactions on Twitter; they can extend to more valuable scenarios. This includes content production such as music and video, as well as investment analysis and fund management services that are more aligned with the needs of cryptocurrency users. Starting from this stage, AI Agents will separate from meme coins, thus forming a brand new track.
Representative Project:
· 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: Recent market fluctuations have been significant, and the cryptocurrencies mentioned in this article have experienced varying degrees of rise and fall. Therefore, the data in this article may differ from current data. This article is for reference only and does not constitute investment advice.)
Special Edition: 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 tokens issued by platforms have a wealth effect, users will continue to seek and purchase the tokens issued by that platform. The real returns generated from users' purchases will empower the platform tokens to drive price increases. As the prices of platform tokens continue to rise, capital will overflow to the tokens issued under them, forming a wealth effect.
The business model is clear and has a positive flywheel effect. However, 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. When the functions are similar, the competition will be based on the quality of the projects under it. If a single platform can consistently produce high-quality projects and has a wealth effect, users' stickiness to that issuing platform will naturally increase, making it difficult for other projects to seize users.
Representative Project:
· 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 to explore collaborations between projects to build a more robust ecosystem. The focus of this stage is on interoperability and the expansion of the ecosystem network, especially whether they can create synergies 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 first necessary to establish a standardized framework, providing developers with predefined components, abstract concepts, and relevant tools to simplify the development process of complex AI Agents. By proposing standardized solutions for common challenges encountered during AI Agent development, these frameworks can help developers focus on the uniqueness of their applications rather than starting from scratch each time to design infrastructure, thus avoiding the problem of reinventing the wheel.
Representative Project:
· 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 level, AI Agents may more often serve 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. 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 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 such as arbitrage strategies, asset rebalancing, and risk hedging. AI Agents can significantly enhance the competitiveness of funds.
Representative Project:
· 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 implemented in our daily applications. For example, the AI Agent I use most often is still the Web 2 Perplexity, and occasionally I look at analysis tweets from AIXBT. Apart from that, the usage frequency of Crypto AI Agents is extremely low, so the fourth stage may remain stagnant for a while, as the product aspect is still immature.
The author believes that 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 Agentnomics. The development of this stage involves not only technological evolution but also redefining the token economic relationships between distributors, platforms, and Agent vendors to create a brand new ecosystem. The main characteristics of this stage are as follows:
1. Analogous to the historical development of the internet
The formation process of Agentnomics can be likened to the evolution of the internet economy, such as the birth of super applications like WeChat and Alipay. These applications integrate platform economies, bringing independent applications into their ecosystems and becoming multifunctional gateways. In this process, a collaborative and symbiotic economic model is formed between application providers and platforms. AI Agents will also replay a similar process in the fifth stage but based on cryptocurrency and decentralized technology.
2. Reshape 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 professional application marketplaces or DApp ecosystems.
· Platform: Provides infrastructure and a collaborative framework that allows multiple Agent vendors to operate in a unified environment, responsible for managing ecosystem rules 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 benefits between distributors, platforms, and vendors will achieve decentralized distribution, such as revenue-sharing mechanisms, contribution rewards, and governance rights, thus promoting collaboration and incentivizing innovation.
3. The entrance and integration of super applications
When AI Agents evolve into super application gateways, 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. The super application of AI Agents will further break traditional application silos.
This article comes from contributions and does not represent the views of BlockBeats.
