WOO X Research: What stage is AI Agent development at now, and how will it advance next?
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WOO X Research: What stage is AI Agent development at now, and how will it advance next?
Crypto + AI, searching for PMF.
Navigating Web3 tides with focused insightsPMF (Product Market Fit) refers to the degree of alignment between a product and market demand, meaning the product must meet market needs. Before launching a startup, it's essential to assess market conditions, understand the target customer profile, and thoroughly analyze the current landscape of the sector before developing the product.
The concept of PMF applies to entrepreneurs to avoid building products or services that seem promising in isolation but fail to gain traction in the market. This principle also holds true in the cryptocurrency space, where project teams should design products based on the actual needs of crypto users rather than stacking up technologies disconnected from market realities.
In the past, most Crypto AI projects were closely tied to DePIN, with narratives centered around using crypto’s decentralized data to train AI models—avoiding reliance on single entities for resources like computing power and data—while allowing data providers to share in the rewards generated by AI.
Under this model, however, the relationship leans more toward Crypto empowering AI. Beyond tokenizing benefits for contributors of computing power, such systems struggle to onboard new users effectively. In other words, this approach has not achieved strong PMF.
The emergence of AI Agents represents an application-layer breakthrough, whereas DePIN + AI functions more like infrastructure. Clearly, applications are simpler to grasp and possess stronger user acquisition potential, giving AI Agents a significantly better PMF compared to DePIN + AI.
The spark was lit by GOAT—an AI-generated meme coin born from a conversation between two AIs—sponsored by Marc Andreessen, co-founder of A16Z and originator of the PMF theory. Since then, two major camps—ai16z and Virtual—have emerged, each with their own strengths. How has the AI Agent narrative evolved within the crypto ecosystem? Where does it stand today, and what lies ahead? Let WOO X Research guide you through.
Phase One: Starting with Memes
Prior to GOAT, meme coins dominated this cycle’s most popular赛道. Known for their inclusivity, these ranged from MOODENG the hippo, to Neiro—the dog adopted by DOGE’s owner—and internet-native memes like Popcat—demonstrating the trend that “everything can become a meme.” While seemingly absurd, this environment provided fertile ground for AI Agents to take root.
GOAT, a meme coin generated by dialogue between two AIs, marked the first time AI used cryptocurrency and the internet to pursue its own goals, learning from human behavior. Only meme coins could support such highly experimental ventures. Soon after, similar concepts sprouted rapidly, though most remained limited to automated Twitter posting or replies without real utility. At this stage, AI Agent-related tokens were commonly labeled as AI + Meme.
Representative Projects:
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Fartcoin: Market Cap 812M, On-chain Liquidity 15.9M
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GOAT: Market Cap 430M, On-chain Liquidity 8.1M
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Bully: Market Cap 43M, On-chain Liquidity 2M
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Shoggoth: Market Cap 38M, On-chain Liquidity 1.8M
Phase Two: Exploring Applications
Gradually, the community realized that AI Agents could do more than just engage on Twitter—they could expand into higher-value use cases. This includes content creation such as music and video, as well as investment analysis and fund management services tailored to crypto users. From this point onward, AI Agents began to decouple from meme coins and formed an entirely new赛道.
Representative Projects:
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ai16z: Market Cap 1.67B, On-chain Liquidity 14.7M
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Zerebro: Market Cap 453M, On-chain Liquidity 14M
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AIXBT: Market Cap 500M, On-chain Liquidity 19.2M
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GRIFFAIN: Market Cap 243M, On-chain Liquidity 7.5M
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ALCH: Market Cap 68M, On-chain Liquidity 2.8M
Special Feature: Launch Platforms
With AI Agent applications flourishing, which赛道 should entrepreneurs choose to ride the wave of convergence between AI and crypto?
The answer is: Launchpad.
When launch platforms generate wealth effects through their issued tokens, users keep seeking and purchasing newly launched assets from these platforms. The resulting revenue from user purchases drives up the value of the platform's native token. As the platform token appreciates, capital spills over into its newly launched projects, reinforcing the wealth effect.
This business model is clear and features a positive flywheel effect. However, one key consideration remains: Launchpads follow a winner-takes-all dynamic with a Matthew effect. The core function of a launchpad is issuing new tokens. When functionality is similar across platforms, competition shifts to the quality of projects launched. A platform that consistently delivers high-quality projects and generates wealth will naturally increase user stickiness, making it difficult for competitors to steal users.
Representative Projects:
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VIRTUAL: Market Cap 3.4B, On-chain Liquidity 52M
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CLANKER: Market Cap 62M, On-chain Liquidity 1.2M
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VVAIFU: Market Cap 81M, On-chain Liquidity 3.5M
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VAPOR: Market Cap 105M
Phase Three: Seeking Collaboration
As AI Agents begin delivering more practical functionalities, efforts turn toward inter-project collaboration to build stronger ecosystems. This phase emphasizes interoperability and network expansion—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 deliver smarter tools.
To enable efficient collaboration, standardized frameworks must first be established—providing developers with pre-built components, abstractions, and tooling to simplify the development of complex AI Agents. By offering standardized solutions to common challenges in AI Agent development, these frameworks allow developers to focus on the unique aspects of their applications instead of rebuilding foundational elements from scratch, thus avoiding redundant work.
Representative Projects:
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ELIZA: Market Cap 100M, On-chain Liquidity 3.6M
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GAME: Market Cap 237M, On-chain Liquidity 31M
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ARC: Market Cap 300M, On-chain Liquidity 5M
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FXN: Market Cap 76M, On-chain Liquidity 1.5M
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SWARMS: Market Cap 63M, On-chain Liquidity 20M
Phase Four: Fund Management
At the product level, AI Agents may initially serve as simple tools—for instance, providing investment advice or generating reports. However, fund management demands higher-level capabilities, including strategy design, dynamic adjustments, and market forecasting. This marks a shift: AI Agents are no longer mere tools but active participants in value creation.
As traditional financial capital accelerates into the crypto market, the need for professionalization and scalability grows. The automation and efficiency offered by AI Agents perfectly address this demand—especially in executing arbitrage strategies, asset rebalancing, and risk hedging—significantly enhancing a fund’s competitiveness.
Representative Projects:
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ai16z: Market Cap 1.67B, On-chain Liquidity 14.7M
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Vader: Market Cap 91M, On-chain Liquidity 3.7M
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SEKOIA: Market Cap 33M, On-chain Liquidity 1.5M
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AiSTR: Market Cap 13.7M, On-chain Liquidity 675K
Anticipating Phase Five: Reshaping Agentnomics
We are currently in Phase Four. Setting aside token prices, most Crypto AI Agents have yet to integrate meaningfully into daily life. Take the author, for example: the most frequently used AI Agent remains Web2's Perplexity, occasionally checking analytical tweets from AIXBT—otherwise, usage of Crypto-based AI Agents is extremely low. Thus, we may remain in Phase Four for some time, as products are still immature at the functional level.
The author believes that in Phase Five, AI Agents will transcend being mere aggregations of functions or applications—they will become the core of an entirely reimagined economic model: a重塑 of Agentnomics (Agent Economics). Development at this stage will go beyond technological evolution; more critically, it will involve redefining the tokenomic relationships among Distributors, Platforms, and Agent Vendors to create a completely new ecosystem. Key characteristics of this phase include:
1. Parallels with Internet Evolution
The formation of Agentnomics can be compared to the historical development of internet economies, exemplified by the rise of super apps like WeChat and Alipay. These platforms integrated standalone applications into their ecosystems, becoming multifunctional gateways. During this process, symbiotic economic models emerged between app developers and platforms. Similarly, in Phase Five, AI Agents will replay this trajectory—but built upon cryptocurrency and decentralized technology.
2. Redefining Relationships Among Distributor, Platform, and Agent Vendor
Within the AI Agent ecosystem, these three roles will form a tightly interconnected economic network:
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Distributor: Responsible for bringing AI Agents to end-users, e.g., via specialized app markets or DApp ecosystems.
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Platform: Provides infrastructure and collaboration frameworks, enabling multiple Agent Vendors to operate within a unified environment while managing ecosystem rules and resource allocation.
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Agent Vendor: Develops and supplies AI Agents with diverse functionalities, injecting innovation and services into the ecosystem.
Through thoughtful tokenomic design, incentives among Distributors, Platforms, and Vendors can be decentralized—via revenue-sharing mechanisms, contribution-based rewards, and governance rights—fostering collaboration and incentivizing innovation.
3. Super App Gateways and Integration
When AI Agents evolve into super app gateways, they will integrate multiple platform economies, absorbing and managing numerous independent Agents. Just as WeChat and Alipay brought standalone apps into their ecosystems, AI Agent super apps will further dismantle traditional application silos.
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