What stage of development is AI Agent at now? What’s the next step?

Author: WOO X

Background: Crypto + AI, looking for PMF

PMF (Product Market Fit) refers to product market fit, which means that the product must meet market demand. Before starting a business, confirm the market situation, understand what type of customers you want to sell to, and understand the current market environment before proceeding. product development.

The concept of PMF is suitable for entrepreneurs to avoid creating products/services that they feel good about but the market does not pay for. This concept also applies to the cryptocurrency market. Project parties should understand the needs of players in the currency circle to create products, rather than stacking technology and The market is out of touch.

In the past, Crypto AI was mostly bundled with DePIN. The narrative was to use Crypto’s distributed data to train AI, thereby avoiding reliance on the control of a single entity, such as computing power, data, etc., while data providers were able to share the benefits brought by AI. income.

According to the above logic, it is actually more like Crypto empowering AI. In addition to tokenizing the benefits and allocating benefits to computing power providers, AI is difficult to Onboard more new users. It can also be said that this model is not so successful in PMF.

The emergence of AI Agent is more like the application side, compared to DePIN

• AI, it is like infrastructure. Obviously, the application is simpler and easier to understand, and has the ability to better attract users, and has a better PMF than DePIN + AI.

First, GOAT, sponsored by A16 Z founder Marc Andreessen (the PMF theory was also proposed by him) and generated by the dialogue between two AIs, launched the first shot of AI Agent. Now, both ai16 z and Virtual camps have their own advantages and disadvantages. What is the development trajectory of AI Agent in the currency circle? What stage is it at now? Where will the future go? Let WOO X Research show you.

Phase One: Meme Starts

Before the emergence of GOAT, the most popular track this cycle was meme coins, and the characteristic of meme coins is their strong inclusiveness, from the zoo’s hippo MOODENG, to the DOGE owner’s newly adopted Neiro, the Internet’s native meme Popcat, etc. , showing the trend of "everything can be a meme", and this seemingly nonsensical narrative actually provides the soil for the growth of AI Agents.

GOAT is a meme coin generated by the conversation between two AIs. This is also the first time that AI achieves its goals through cryptocurrency and the Internet and learns from human behavior. Only meme coins can carry such a highly experimental project. At the same time, similar concept coins have sprung up, but most of their functions are limited to automatic tweeting, replying, etc., with no practical applications. At this time, AI Agent coins This kind is often called AI + Meme.

Representative projects:

• Fartcoin: Market value 812M, on-chain liquidity 15.9M
• GOAT: Market value 430M, on-chain liquidity 8.1M
• Bully: Market value 43M, on-chain liquidity 2M
• Shogoth: Market value 3 8M, on-chain liquidity 1. 8M

Phase Two: Exploring Applications

Gradually, everyone realized that AI Agent can not only conduct simple interactions on Twitter, but can be extended to more valuable scenarios. This includes content production such as music videos, as well as investment analysis, fund management and other services that are more suitable for currency users. From this stage, AI Agent is separated from meme coins, thus forming a new track.

Representative projects:

• ai16 z: Market value 1.67B, on-chain liquidity 14.7M
• Zerebro: Market value 453M, on-chain liquidity 14M
• AIXBT: Market value 500M, on-chain liquidity 19.2M
• GRIFFAIN: Market value 243M, on-chain liquidity 7.5M
• ALCH: Market value 6.8M, on-chain liquidity 2.8M

Extra: Distribution platform

When AI Agent applications flourish, what track should entrepreneurs choose to seize this wave of AI and Crypto?

The answer is Launchpad

When the issuing platform's currency has a wealth effect, users will continue to search for and purchase tokens issued by the platform, and the real income generated by users' purchases will also empower the platform currency to drive price increases, and the price of the platform currency will continue to increase. If it rises, funds will overflow to the underlying currency, creating a wealth effect.

The business model is clear and has a positive flywheel effect, but we still need to pay attention to the following: Launchpad is a winner-take-all Matthew effect. The core function of Launchpad is to issue new tokens. In the case of similar functions, the competition is It is the quality of its projects. If a single platform can stably produce high-quality projects and have a wealth-creating effect, users' stickiness to the distribution platform will naturally increase, and it will be difficult for other projects to compete for users.

Representative projects:

• VIRTUAL: Market value 3.4B, on-chain liquidity 52M
• CLANKER: Market value 6 2M, on-chain liquidity 1. 2M
• VVAIFU: Market value 81M, on-chain liquidity 3.5M
• VAPOR: Market value 105M

The third stage: seeking collaboration

After AI Agent begins to implement more practical functions, it begins to explore collaboration between projects to build a more powerful ecosystem. The focus of this stage is on interoperability and the expansion of the ecological network, especially whether it can create synergies with other encryption projects or protocols. For example, AI Agent may cooperate with DeFi protocols to improve automated investment strategies, or integrate with NFT projects to implement smarter tools.

To achieve efficient collaboration, we first need to establish a standardized framework to provide developers with preset components, abstract concepts, and related tools to simplify the development process of complex AI Agents. By proposing standardized solutions to common challenges in AI Agent development, these frameworks help developers focus on the uniqueness of their applications rather than reinventing the wheel every time by designing the infrastructure from scratch. question.

Representative projects:

• ELIZA: Market value 100M, on-chain liquidity 3.6M
• GAME: Market value 237M, on-chain liquidity 31M
• ARC: Market value 300M, on-chain liquidity 5M
• FXN: Market value 76M, on-chain liquidity 1.5M
• SWARMS: Market value 63M, on-chain liquidity 20M

Stage 4: Fund Management

From a product level, AI Agent may serve more as a simple tool, such as giving investment advice and generating reports. However, fund management requires higher-level capabilities, including strategy design, dynamic adjustment and market prediction, which marks that AI Agent is not just a tool, but begins to participate in the process of value creation.

As traditional financial funds accelerate their entry into the crypto market, the demand for specialization and scale continues to increase. The automation and high efficiency of AI Agent can just meet this demand. Especially when performing functions such as arbitrage strategies, asset rebalancing and risk hedging, AI Agent can significantly enhance the competitiveness of funds.

Representative projects:

• ai16z: Market value 1.67B, on-chain liquidity 14.7M
• Vader: Market value 91M, on-chain liquidity 3.7M
• SEKOIA: Market value 33M, on-chain liquidity 1.5M
• AiSTR: Market value 13.7M, on-chain liquidity 675K

Looking forward to the fifth phase: Reinventing Agentnomics

We are currently in the fourth stage. Regardless of the currency price, most of the current Crypto AI Agents have not been implemented in our daily life applications. Taking the author as an example, the most commonly used AI Agent is Perplexity of Web 2, and occasionally Looking at the analysis tweets of AI

The author believes that in the fifth stage, AI Agent is not just an aggregation of functions or applications, but the core of the entire economic model - the reshaping of Agent economics. The development at this stage not only involves technological evolution, but more importantly, redefines the token economic relationship between Distributor, Platform and Agent Vendor to create a new ecosystem. The following are the main characteristics of this stage:

1. Analogy to the development history of the Internet

The formation process of Agent nomics can be compared to the evolution of the Internet economy, such as the birth of super applications such as WeChat and Alipay. By integrating the platform economy, these applications introduce independent applications into their own ecology and become multi-functional portals. In this process, an economic model of collaboration and symbiosis is formed between application providers and platforms, and AI Agent will repeat a similar process in the fifth phase, but based on cryptocurrency and decentralized technology.

2. Reshape the relationship between distributors, platforms and agent suppliers

In the ecosystem of AI Agent, the three will establish a closely connected economic network:

• Distributor: Responsible for promoting AI Agent to end users, such as through a professional application market or DApp ecosystem.
• Platform (Platform): Provides infrastructure and collaboration framework, allows multiple Agent suppliers to run in a unified environment, and is responsible for managing ecological rules and resource allocation.
• Agent Vendor: Develop and provide AI Agents with different functions to deliver innovative applications and services to the ecosystem.

Through the token economic design, benefits among distributors, platforms and suppliers will be distributed in a decentralized manner, such as sharing mechanisms, contribution returns and governance rights, thereby promoting collaboration and stimulating innovation.

3. Entrance and integration of super applications

When AI Agent evolves into a super application portal, it will be able to integrate multiple platform economies and absorb and manage a large number of independent Agents. This is similar to how WeChat and Alipay integrate independent applications into their ecosystems. AI Agent's super applications will further break down traditional application islands.