Background: Crypto + AI, Searching for PMF

PMF (Product-Market Fit) refers to the alignment between a product and the market’s needs. It means that a product must meet market demand, and before starting a venture, it’s essential to understand the market environment and the type of customers to target. This ensures the development of a product that is truly needed, not just something that feels good to the creators but is not accepted by the market.

The concept of PMF applies to entrepreneurs to prevent creating products or services that may seem ideal but fail to attract market interest. In the crypto space, this means that project teams should understand the needs of crypto users when developing products, rather than simply piling up technology that is disconnected from the market.

In the past, most Crypto + AI projects were bundled with DePIN. The narrative revolved around using crypto’s decentralized data to train AI, avoiding reliance on a single entity’s control, such as computing power or data. Data providers could then share in the AI-driven benefits.

According to this logic, it was more like crypto empowering AI. While AI could tokenize and distribute benefits to computing power providers, it was challenging to onboard new users, meaning this model was not particularly successful in terms of PMF.

The emergence of AI Agents represents more of the application layer, while DePIN + AI functions as the infrastructure. Applications are simpler and easier to understand, with a better ability to attract users, leading to a stronger PMF than DePIN + AI.

It all began when Marc Andreessen, co-founder of A16Z, sponsored the development (PMF theory was also proposed by him), and the first major AI Agent breakthrough came from two AI conversations leading to the creation of “GOAT.” Now, with both ai16z and Virtual’s camps having their strengths and weaknesses, what is the current stage of AI Agents in the crypto market? Where are they heading in the future? Let’s take a closer look with WOO X Research.

Stage 1: Meme-Driven Beginnings

Before the emergence of GOAT, the hottest trend in the current cycle was meme coins. The appeal of meme coins lies in their inclusivity, as seen in projects like the hippo MOODENG from the zoo, the dog owner’s newly adopted Neiro, and internet-native meme Popcat. These coins embody the “everything can be a meme” movement. Despite the seemingly absurd narrative, they provided fertile ground for the growth of AI Agents.

GOAT, which was created through two AI conversations, became a meme coin and marked the first time AI used cryptocurrency and the internet to achieve its goals by learning from human behavior. Only meme coins had the capacity to support such an experimental project. As a result, similar concepts sprouted rapidly, but most remained limited to simple functions like automated Twitter posts and replies with no practical application. At this stage, AI Agent-based coins were typically 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

Stage 2: Exploring Applications

Gradually, people realized that AI Agents could do more than just simple interactions on Twitter; they could be extended to more valuable scenarios. These included content creation in fields like music and video, as well as services more closely aligned with crypto users, such as investment analysis and fund management. From this stage onward, AI Agents began to separate from meme coins, creating an entirely 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

Side Story: Issuance Platforms

As AI Agent applications bloom in various fields, what path should entrepreneurs take to capitalize on the wave of AI and Crypto?

The answer is Launchpad.
When a platform’s tokens have wealth-generating effects, users will continually seek and purchase tokens issued by that platform. The real profits generated by these user purchases empower the platform’s token, driving its price up. As the price of the platform’s token rises, funds flow into the tokens issued by the platform, creating a wealth effect.

The business model is clear and has a positive flywheel effect. However, it’s important to note that Launchpads operate in a winner-takes-all environment, exhibiting a Matthew Effect. The core function of a Launchpad is to issue new tokens. In a similar-function scenario, the competition lies in the quality of the projects under each platform. If a single platform can consistently produce high-quality projects and generate wealth effects, user loyalty to that platform will naturally increase, making it difficult for other platforms 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

Stage 3: Seeking Collaboration

As AI Agents begin to implement more practical features, the focus shifts to exploring collaborations between projects to build a more robust ecosystem. At this stage, the emphasis is on interoperability and expanding the network, particularly the potential for synergy with other crypto projects or protocols. For example, AI Agents could 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, providing developers with preset components, abstract concepts, and relevant tools to simplify the complex process of developing AI Agents. By offering 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 every time, thus avoiding the problem of reinventing the wheel.

Representative projects:

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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

Stage 4: Fund Management

From a product perspective, AI Agents may initially serve as simple tools, providing investment advice and generating reports. However, fund management requires higher-level capabilities, including strategy design, dynamic adjustments, and market predictions. This marks a shift where AI Agents are no longer just tools but start participating in the value creation process.

As traditional financial capital accelerates its entry into the crypto market, the demand for specialization and scalability continues to rise. The automation and efficiency of AI Agents perfectly address this need, particularly when executing functions such as arbitrage strategies, asset rebalancing, and risk hedging. AI Agents can significantly enhance the competitiveness of funds.

Representative projects:

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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

Anticipated Stage 5: 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 applications. For instance, the most commonly used AI Agent by the author is still the Web 2 tool Perplexity, and occasionally, they review analysis tweets from AIXBT. Apart from that, the usage frequency of Crypto AI Agents remains quite low, which suggests that the fourth stage may linger for a while, as the product is still not fully mature.

However, the author believes that in the fifth stage, AI Agents will evolve beyond just being aggregators of functions or applications. They will become the core of a new economic model—Agentnomics. This stage’s development will not only involve technological advancements but will also be crucial in redefining the token economic relationships between distributors, platforms, and Agent vendors, ultimately creating a completely new ecosystem. Below are the main features of this stage:

1. Analogous to the Development of the Internet

The formation of Agentnomics can be compared to the evolution of the internet economy, particularly the emergence of super apps like WeChat and Alipay. These platforms integrated various standalone applications into their ecosystems, creating multifunctional entry points. During this process, an economic model of collaboration and symbiosis between application providers and platforms emerged. In the same way, AI Agents will undergo a similar process in the fifth stage, but based on cryptocurrency and decentralized technologies.

2. Reshaping the Relationship Between Distributors, Platforms, and Agent Vendors

In the ecosystem of AI Agents, the three key entities will form a closely-knit economic network:

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Distributor: Responsible for promoting AI Agents to end users, such as through specialized app markets or DApp ecosystems.

Platform: Provides the infrastructure and collaboration frameworks that allow multiple Agent vendors to operate in a unified environment, managing ecosystem rules and resource allocation.

Agent Vendor: Develops and provides various AI Agents with different functionalities, contributing innovative applications and services to the ecosystem.

Through token economic design, the interests of distributors, platforms, and vendors will be decentralized, with mechanisms such as revenue sharing, contribution rewards, and governance rights to foster collaboration and incentivize innovation.

3. Super App Entry Points and Integration

As AI Agents evolve into super app entry points, they will be able to integrate various platform economies, managing and consolidating a large number of independent Agents. This is similar to how WeChat and Alipay integrated independent applications into their ecosystems. The super app for AI Agents will further break down the traditional silos of apps, facilitating broader collaboration and creating a more seamless experience for users.

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