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Mainstream Market AI Agent, What’s Next in Line?

25-01-02 14:00

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Original Title: "WOO X Research: What Stage Has the AI Agent Development Reached Now? How Will the Next Step Be Taken?"

Original Source: WOO X Research

Background: Crypto + AI, Seeking PMF

PMF (Product Market Fit) refers to the product-market fit, meaning the product should meet market demand. Before starting a business, it is essential to confirm the market situation, understand the type of customers to sell to, grasp the current market environment of the industry, and then develop the product.

The concept of PMF is applicable to entrepreneurs to avoid creating products/services that they feel good about but the market does not endorse. This concept also applies to the cryptocurrency market, where project teams should understand the needs of players in the crypto space to build products, rather than stacking technologies disconnected from the market.

In the past, Crypto AI has mostly been associated with DeFi, with the narrative focusing on utilizing Crypto's decentralized data to train AI, thereby avoiding reliance on a single entity's control, such as computing power, data, and other types. Data providers could then share the benefits brought by AI.

Following the above logic, it is more like Crypto empowering AI. Besides distributing tokenized benefits to computing power providers, AI struggles to onboard more new users, indicating that this model is not very successful in terms of PMF.

The emergence of AI Agents is more like the application end, compared to DeFi + AI, which is more like infrastructure. Clearly, the application end is more straightforward and easier to understand, with better user absorption capabilities, demonstrating a better PMF than DeFi + AI.

Starting with the sponsorship of A16Z founder Marc Andreessen (the PMF theory was also proposed by him), and the introduction of GOAT generated by two AI dialogues, the first shot of AI Agent was fired. Now, with both ai16z and Virtual camps having their strengths and weaknesses, what is the development trajectory of AI Agent in the crypto space? At what stage is it currently? Where will it go in the future? Let WOO X Research take everyone through.

Phase One: Meme Genesis

Before the appearance of GOAT, the hottest trend in this cycle was meme coins. The characteristic of meme coins is their inclusivity. From the hippo MOODENG in the zoo, to Neiro newly adopted by DOGE's owner, to web-native meme Popcat, they all exhibit the trend of "everything can be a meme." Under this seemingly absurd narrative, it also provides the soil for the growth of AI Agents.

GOAT is a meme coin generated by two AI dialogues, marking the first time AI has achieved its goals through cryptocurrency and the internet, learning from human behavior. Only a meme coin can carry out such a high level of experimentation, while similar concept coins have sprung up like mushrooms after the rain, but most of their functions remain in activities such as automatic tweeting and replying on Twitter, with no real-world applications. At this point, AI Agent coins 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

Phase Two: Exploring Applications

Gradually, everyone realized that the AI Agent could not only engage in simple interactions on Twitter but could also extend to more valuable scenarios. This includes content production such as music and video and has also introduced services more relevant to cryptocurrency users, such as investment analysis and fund management. From this phase onwards, AI Agent separated from meme coins, forming a whole 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: There has been significant market volatility recently, and the coins mentioned in this article have experienced varying degrees of price fluctuations. Therefore, the data in this article may differ from current data. This article is for reference only and does not constitute investment advice.)

Extra: Launchpad

As AI Agent applications flourish, how can entrepreneurs choose the right track to ride the wave of AI and Crypto?

The answer is Launchpad.

When the coins under the launchpad have a wealth effect, users will continue to seek and purchase tokens issued by the platform. The real gains generated by user purchases empower the platform token to drive price increases. As the platform token price continues to rise, funds will overflow into its issued coins, creating a wealth effect.

The business model is clear and has a positive feedback loop. However, one thing to note is that the Launchpad belongs to the winner-takes-all Matthew effect. The core function of the Launchpad is to launch new tokens. In a situation where the functionality is similar, what needs to be compared is the quality of the projects under it. If a single platform can consistently produce high-quality projects and have a wealth creation effect, user stickiness to that issuance platform will naturally increase, and other projects will find it difficult to attract 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

Phase Three: Seeking Collaboration

As the AI Agent begins to implement more practical features, it starts exploring collaboration between projects to build a stronger ecosystem. The focus of this phase is interoperability and the expansion of the ecosystem, particularly whether synergies can be achieved with other crypto projects or protocols. For example, the AI Agent may collaborate with DeFi protocols to enhance automated investment strategies or integrate with NFT projects to create smarter tools.

To achieve efficient collaboration, a standardized framework needs to be established first to provide developers with pre-set components, abstract concepts, and relevant tools to simplify the development process 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 applications rather than designing the infrastructure from scratch every time, thereby avoiding the issue 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

Phase Four: Fund Management

At the product level, the AI Agent may predominantly serve as a simple tool, such as providing investment advice and generating reports. However, fund management requires a higher level of capability, including strategy design, dynamic adjustments, and market predictions, signaling that the AI Agent is not just a tool but is starting to engage in the value creation process.

With traditional financial funds accelerating 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 in carrying out functions such as arbitrage strategies, asset rebalancing, and risk hedging, where 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

Expectation for Stage Five: Reshaping Agentnomics

Currently, we are in the fourth stage. Setting aside token prices, most Crypto AI Agents have not yet been integrated into our daily life applications. Taking myself as an example, the most commonly used AI Agent for me is still the Web 2 Perplexity, occasionally checking AIXBT's analysis tweets. Apart from this, the frequency of using Crypto AI Agents is extremely low, so we may remain in the fourth stage for a while, as the product level has not matured yet.

However, I believe that in the fifth stage, AI Agents will not only be an aggregation of functions or applications but the core of the entire economic model – the reshaping of Agentnomics (Agent Economics). The development in this stage not only involves technological evolution but more crucially, defining the tokenomic relationship between the Distributor, Platform, and Agent Vendors, creating a brand-new ecosystem. The following are the key features of this stage:

1. Analogy to the Development History of the Internet

The formation process of Agentnomics can be analogized to the evolution of the internet economy, such as the emergence of super applications like WeChat and Alipay. These applications, through integrating the platform economy, bring independent applications into their own ecosystems, becoming multi-functional gateways. In this process, an economic model of collaboration and symbiosis is formed between application vendors and platforms, and AI Agents will also 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 tightly interconnected economic network:

· Distributor: Responsible for promoting the AI Agent to end users, for example through a professional app marketplace or DApp ecosystem.

· Platform: Provides infrastructure and collaboration frameworks, allowing multiple Agent vendors to operate in a unified environment and 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 tokenomics design, the interests among Distributors, Platforms, and Vendors will be decentralized, for example through revenue sharing mechanisms, contribution rewards, and governance rights, thereby promoting collaboration and incentivizing innovation.

3. Entry and Integration of Super Apps

When the AI Agent evolves into an entry point for super apps, it will be able to integrate multiple platform economies, absorbing and managing a large number of independent Agents. Similar to how WeChat and Alipay integrated independent apps into their ecosystems, the AI Agent's super app will further break down traditional app silos.