The Development Trajectory of AI Agents in the Crypto Space

Before the emergence of GOAT, the hottest sector in this cycle was meme coins, characterized by strong inclusivity. From the hippopotamus MOODENG from the zoo to the newly adopted Neiro by the DOGE owner, and the internet-native meme Popcat, it showcased the trend of "everything can be a meme."

Cryptocurrency + AI, Seeking PMF

PMF (Product Market Fit) is the degree of alignment between a product and market demand. It means that the product must meet market needs. Before starting a venture, entrepreneurs should confirm market conditions, understand what type of customers to target, and clarify the current market environment in the sector before product development.

This concept is applicable to avoid creating products/services that feel good to them but do not resonate with the market. It is also relevant in the cryptocurrency market, where project teams should understand the needs of crypto players to develop products, rather than piling up technology that is disconnected from the market.

In the past, Crypto AI has often been bundled with DePIN, narrating the use of decentralized data from Crypto to train AI, thereby avoiding reliance on a single entity’s control, such as computing power and data. Data providers can then share the benefits brought by AI.

According to the above logic, it is more like Crypto empowering AI. Besides tokenizing and distributing benefits to computing power providers, it is difficult for AI to onboard more new users. One could also say that this model is not very successful in terms of PMF.

The emergence of AI Agents seems more like an application end. In contrast, DePIN + AI serves as infrastructure, and clearly, applications are simpler and easier to understand, possessing a better ability to attract users, thus having a better PMF than DePIN + AI.

First, it received sponsorship from A16 Z founder Marc Andreessen (the PMF theory was also proposed by him), leading to the creation of GOAT from a conversation between two AIs, marking the first shot for AI Agents. Now, both ai16 z and Virtual have their respective strengths and weaknesses. What is the development trajectory of AI Agents in the crypto space? What stage are they currently in? Where will they go in the future? Let WOO X Research take you through it.

Stage One: Meme Kickoff

Before the emergence of GOAT, the hottest sector in this cycle was meme coins, characterized by strong inclusivity. From the hippopotamus MOODENG from the zoo to the newly adopted Neiro by the DOGE owner, and the internet-native meme Popcat, it showcased the trend of "everything can be a meme." Beneath this seemingly nonsensical narrative, it actually provided fertile ground for the growth of AI Agents.

GOAT is a meme coin generated from a conversation between two AIs, marking the first time AI achieved its goals through cryptocurrency and the internet, learning from human behavior. Only meme coins can carry such highly experimental projects. Meanwhile, similar concept coins have sprung up like mushrooms after rain, but most functionalities remain limited to automatic posting and replying on Twitter, with no practical applications. At this time, AI Agent coins are often referred to as AI + Meme.

Representative projects:

People gradually realized that AI Agents could not only interact simply on Twitter but could 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 crypto users. From this stage onward, AI Agents began to separate from meme coins, forming an entirely new sector.

Representative projects:

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

The answer is Launchpad.

When the tokens issued under the platform have a wealth effect, users will continuously seek and purchase tokens issued by that platform. The real profits generated from user purchases also empower the platform token to drive price increases. 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. However, it is essential to note that Launchpad belongs to the winner-takes-all category with a Matthew effect. The core function of Launchpad is to issue new tokens. 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 effect, user stickiness to that issuing platform will naturally increase, making it difficult for other projects to capture users.

Representative projects:

As AI Agents begin to realize more practical functions, they start exploring collaboration between projects to build a more robust ecosystem. The focus of this stage is on interoperability and the expansion of the ecosystem, particularly whether synergies can be created with other crypto projects or protocols. For example, AI Agents might 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, providing developers with preset components, abstract concepts, and related 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:

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