The Development Trajectory of AI Agent in the Cryptocurrency Circle

This article introduces the development trajectory of AI Agent in the cryptocurrency circle, and analyzes the current stage and future development direction of AI Agent.

Product-market fit (PMF) is a crucial concept in entrepreneurship, ensuring that a product aligns with market demand. Before launching a venture, it's essential to assess the market landscape, identify the target customer base, and understand the competitive environment. This approach helps avoid creating products or services that entrepreneurs find appealing but the market ultimately rejects.

The PMF concept also applies to the cryptocurrency domain. Project teams should prioritize understanding the needs of crypto enthusiasts and tailoring products accordingly, rather than piling on technology that's out of sync with market demands.

In the past, crypto AI was primarily integrated with DePIN. The narrative centered on utilizing crypto's decentralized data to train AI, eliminating reliance on a single entity for computing power, data, and other resources. This approach aimed to distribute the benefits of AI to data providers.

However, according to this logic, it seems like crypto is empowering AI. While this integration might benefit computing power providers by distributing profits in the form of tokens, it doesn't inherently onboard more new users to AI. It could even be argued that this model has not achieved optimal PMF.

The emergence of AI Agent is more like an application end, compared to DePIN + AI which is like infrastructure. It goes without saying that applications are simpler, easier to understand, and have a better ability to attract users, ultimately achieving better PMF than DePIN + AI.

GOAT, sponsored by A16Z founder Marc Andreessen (who also proposed the PMF theory), was created by a conversation between two AIs, marking the starting point of AI Agent. Currently, both ai16z and Virtual have their own strengths and weaknesses. So, what is the development trajectory of AI Agent in the crypto space? What stage is it in now? And where will it go in the future? Let WOO X Research show you.

Phase 1: Meme StartsBefore the emergence of GOAT, the hottest track in this cycle was meme coins, and one of the characteristics of meme coins is their strong inclusiveness. From the zoo's hippo MOODENG, to the newly adopted Neiro of DOGE's owner, to the Internet's native meme Popcat, etc., they show the trend of "everything can be a meme". Such seemingly nonsensical narratives actually provide a fertile soil for the growth of AI Agents.

GOAT is a meme coin generated by a conversation between two AIs. This is the first time that AI has achieved its own goals through cryptocurrency and the Internet and learned 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 remain at automatic Twitter posting and replying, etc., without practical applications. At this time, AI Agent coins are usually called AI + Meme.

Representative projects:

Phase 2: Exploring ApplicationsGradually, people realized that AI Agents can not only interact with each other on Twitter, but also extend to more valuable scenarios. This includes content production such as music and video, as well as investment analysis, fund management and other services that are more suitable for cryptocurrency users. From this stage, AI Agents separated from meme coins, forming a new track.

Representative projects:

Extra: Distribution PlatformAs AI Agent applications flourish, which track should entrepreneurs choose to seize this wave of AI and Crypto?

The Answer is LaunchpadWhen the currency under the issuing platform has a wealth effect, users will continue to look for and purchase tokens issued by the platform. The real income generated by the users' purchases will also enable the platform currency to drive up the price. As the price of the platform currency continues to rise, funds will spill over to the currency issued under it, forming a wealth effect.

The business model is clear and has a positive flywheel effect, but it should be noted that Launchpad is a winner-takes-all Matthew effect. The core function of Launchpad is to issue new tokens. Under similar functions, what needs to be competed is the quality of the projects under it. If a single platform can stably produce high-quality projects and has a wealth-creating effect, the user's stickiness to the issuance platform will naturally increase, and it will be difficult for other projects to snatch users.

Representative projects:

Phase 3: Seeking collaborationAfter AI Agent begins to realize more practical functions, it will begin to explore collaboration between projects and build a stronger ecosystem. The focus of this stage is on interoperability and the expansion of the ecological network, especially whether it can generate synergies with other crypto projects or protocols. For example, AI Agent may cooperate with DeFi protocols to improve automated investment strategies, or integrate with NFT projects to achieve smarter tools.

To achieve efficient collaboration, it is necessary to first 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 can help developers focus on the