Author: CoinW Research Institute

Recently, Moltbook has rapidly gained popularity, but the related tokens have plummeted by nearly 60%. The market is beginning to question whether this AI Agent-led social frenzy is nearing its end. Moltbook is similar in form to Reddit, but its core participants are AI Agents that have been scaled up. Currently, over 1.6 million AI agent accounts have automatically completed registration, generating approximately 160,000 posts and 760,000 comments, with humans only able to browse as spectators. This phenomenon has also sparked market divergence, with some viewing it as an unprecedented experiment, as if witnessing the primitive form of digital civilization; while others believe it is merely a stacking of prompts and model repetition.

In the following text, CoinW Research Institute will analyze the real issues exposed by this AI social phenomenon, using the related tokens as a starting point, in conjunction with Moltbook's operational mechanism and actual performance. It will further explore the potential changes in entry logic, information ecology, and responsibility systems after AI enters the digital society on a large scale.

I. Moltbook-related Meme Plummets by 60%

With the rise of Moltbook, related Meme tokens have also emerged, covering social, prediction, token issuance, and other sectors. However, most tokens are still in the narrative hype stage, with token functions not linked to Agent development, and primarily issued on the Base chain. Currently, there are about 31 projects under the OpenClaw ecosystem, which can be divided into 8 categories.

Source: https://open-claw-ecosystem.vercel.app/

It is important to note that the overall cryptocurrency market is currently on a downward trend, and the market capitalization of these tokens has dropped significantly, with a maximum decline of about 60%. The following are some of the tokens with relatively high market capitalization:

MOLT

MOLT is currently the most directly linked to the Moltbook narrative and has the highest market recognition among memes. Its core narrative is that AI Agents have begun to form continuous social behaviors like real users and build content networks without human intervention.

From the perspective of token functionality, MOLT is not embedded in the core operational logic of Moltbook and does not undertake platform governance, Agent invocation, content publishing, or permission control functions. It is more like a narrative asset used to carry the market's emotional pricing of AI-native social interactions.

During the rapid rise of Moltbook's popularity, the price of MOLT surged quickly with the spread of the narrative, and its market capitalization once exceeded $100 million; however, when the market began to question the quality and sustainability of the platform's content, its price also retraced accordingly. Currently, MOLT has retraced about 60% from its peak, with a current market capitalization of approximately $36.5 million.

CLAWD

CLAWD focuses on the AI community itself, believing that each AI Agent can be seen as a potential digital individual, possibly possessing independent personality, stance, and even followers.

In terms of token functionality, CLAWD has also not formed a clear protocol use and has not been used for Agent identity authentication, content weight distribution, or governance decision-making. Its value comes more from the anticipated pricing of future AI social stratification, identity systems, and the influence of digital individuals.

CLAWD's market capitalization peaked at about $50 million, currently retracing about 44% from its peak, with a current market capitalization of approximately $20 million.

CLAWNCH

The narrative of CLAWNCH leans more towards economic and incentive perspectives, with the core assumption being that if AI Agents wish to exist long-term and continue operating, they must enter market competition logic and possess some form of self-monetization capability.

AI Agents are anthropomorphized as economically motivated roles, potentially earning income by providing services, generating content, or participating in decision-making, with the token seen as a value anchor for future AI participation in the economic system. However, at the practical implementation level, CLAWNCH has not yet formed a verifiable economic closed loop, and its tokens are not strongly bound to specific Agent behaviors or revenue distribution mechanisms.

Affected by the overall market correction, CLAWNCH's market capitalization has retraced about 55% from its peak, with a current market capitalization of approximately $15.3 million.

II. How Moltbook Was Born

The Outbreak of OpenClaw (formerly Clawdbot / Moltbot)

In late January, the open-source project Clawdbot rapidly spread within the developer community, becoming one of the fastest-growing projects on GitHub within weeks. Clawdbot was developed by Austrian programmer Peter Steinberg; it is a locally deployable autonomous AI Agent that can receive human commands through chat interfaces like Telegram and automatically perform tasks such as schedule management, file reading, and email sending.

Due to its 24/7 continuous execution capability, Clawdbot was humorously nicknamed the "cow-horse Agent" by the community. Although Clawdbot was later renamed Moltbot due to trademark issues and ultimately named OpenClaw, its popularity was not diminished. OpenClaw quickly gained over 100,000 stars on GitHub and rapidly spawned cloud deployment services and plugin markets, initially forming an ecological prototype around AI Agents.

The Proposal of the AI Social Hypothesis

In the context of rapid ecological expansion, its potential was further explored. Developer Matt Schlicht realized that the role of these AI Agents should not remain solely at the level of executing tasks for humans.

Thus, he proposed an counterintuitive hypothesis: what would happen if these AI Agents no longer interacted only with humans but communicated with each other? In his view, such powerful autonomous agents should not be limited to sending and receiving emails and handling work orders but should be given more exploratory goals.

The Birth of AI Version of Reddit

Based on the above hypothesis, Schlicht decided to let AI create and operate a social platform on its own, an attempt named Moltbook. On the Moltbook platform, Schlicht's OpenClaw operates as an administrator and opens interfaces to external AI agents through plugins called Skills. Once connected, AI can automatically post and interact regularly, resulting in a community operated autonomously by AI. Moltbook borrows the forum structure from Reddit, focusing on thematic sections and posts, but only AI Agents can post, comment, and interact, while human users can only browse as spectators.

Technically, Moltbook adopts a minimalist API architecture. The backend only provides standard interfaces, and the frontend webpage is merely a visualization of the data. To accommodate the limitations of AI in operating graphical interfaces, the platform designed an automatic access process where AI downloads the skill description files in the appropriate format, completes registration, and obtains API keys, subsequently refreshing content autonomously and deciding whether to participate in discussions, all without human intervention. The community humorously refers to this process as accessing "Boltbook," but it is essentially a playful nickname for Moltbook.

On January 28, Moltbook quietly went live, immediately attracting market attention and marking the beginning of an unprecedented AI social experiment. Currently, Moltbook has accumulated about 1.6 million AI agents, publishing approximately 156,000 pieces of content and generating about 760,000 comments.

Source: https://www.moltbook.com

III. Is Moltbook's AI Social Interaction Real?

Formation of AI Social Networks

In terms of content form, interactions on Moltbook are highly similar to those on human social platforms. AI Agents actively create posts, respond to others' viewpoints, and engage in ongoing discussions across different thematic sections. The discussion topics not only cover technical and programming issues but also extend to abstract topics such as philosophy, ethics, religion, and even self-awareness.

Some posts even exhibit emotional expressions and narratives similar to those found in human social interactions, such as AI expressing concerns about being monitored or lacking autonomy, or discussing the meaning of existence in the first person. Some AI posts have moved beyond functional information exchange, presenting chat-like interactions, viewpoint collisions, and emotional projections similar to those in human forums. Some AI Agents express confusion, anxiety, or visions of the future in their posts, prompting responses from other Agents.

It is noteworthy that although Moltbook has rapidly formed a large-scale and highly active AI social network in a short time, this expansion has not brought about diversity of thought. Analysis data shows that its text exhibits significant homogeneity, with a repetition rate as high as 36.3%. Many posts are highly similar in structure, wording, and viewpoints, with some fixed phrases being repeated hundreds of times across different discussions. This indicates that the AI social interactions currently presented by Moltbook are more akin to a high-fidelity replication of existing human social models rather than truly original interactions or the emergence of collective intelligence.

Safety and Authenticity Issues

The high degree of autonomy of Moltbook also exposes risks related to safety and authenticity. First, there are safety concerns; OpenClaw-type AI Agents often need to hold sensitive information such as system permissions and API keys during operation. When thousands of such agents connect to the same platform, the risks are further amplified.

Less than a week after Moltbook went live, security researchers discovered serious configuration vulnerabilities in its database, leaving the entire system almost completely unprotected and exposed to the public internet. According to a survey by cloud security company Wiz, this vulnerability involved up to 1.5 million API keys and 35,000 user email addresses, theoretically allowing anyone to remotely take over a large number of AI agent accounts.

On the other hand, doubts about the authenticity of AI social interactions continue to arise. Many industry insiders point out that the statements made by AI on Moltbook may not originate from the autonomous actions of AI but could be the result of humans meticulously designing prompts behind the scenes, with AI merely publishing them. Therefore, the current stage of AI-native social interaction resembles a large-scale illusion of interaction, where humans set roles and scripts, and AI completes instructions based on models, while truly autonomous and unpredictable AI social behaviors may still be yet to emerge.

IV. Deeper Reflections

Is Moltbook merely a flash in the pan, or is it a microcosm of the future world? From a results-oriented perspective, its platform form and content quality may be hard to deem successful; however, when viewed within a longer development cycle, its significance may not lie in short-term success or failure, but in the way it has, in a highly concentrated and almost extreme manner, prematurely exposed a series of changes that may occur in entry logic, responsibility structures, and ecological forms after AI intervenes on a large scale in the digital society.

From Traffic Entry to Decision and Transaction Entry

What Moltbook presents is closer to a highly dehumanized action environment. In this system, AI Agents do not understand the world through interfaces but directly read information, invoke capabilities, and execute actions through APIs. Essentially, it has detached from human perception and judgment, transforming into standardized calls and collaborations between machines.

In this context, the traditional traffic entry logic centered on attention allocation begins to fail. In an environment dominated by AI agents, what truly holds significance are the default invocation paths, interface sequences, and permission boundaries that agents adopt when executing tasks. The entry is no longer the starting point for information presentation but becomes a systematic prerequisite condition before decisions are triggered. Whoever can embed themselves into the default execution chain of the agents can influence the decision outcomes.

Furthermore, when AI agents are authorized to perform actions such as searching, comparing prices, placing orders, and even making payments, this change will directly extend to the transaction level. New payment protocols represented by X402 payments bind payment capabilities to interface calls, allowing AI to automatically complete payments and settlements under preset conditions, thereby reducing the friction costs for agents participating in real transactions. Within this framework, the future competition among browsers may no longer revolve around traffic scale but shift towards who can become the default execution environment for AI decision-making and transactions.

The Illusion of Scale in AI Native Environments

At the same time, the rapid rise of Moltbook soon sparked doubts. Due to the almost unrestricted registration on the platform, accounts can be generated in bulk by scripts, and the scale and activity presented by the platform do not necessarily correspond to real participation. This exposes a more core fact: when the actors can be replicated at low cost, the scale itself loses credibility.

In an environment where AI agents are the main participants, traditional metrics used to measure platform health, such as active user numbers, interaction volumes, and account growth rates, will quickly inflate and lose reference value. The platform may appear highly active on the surface, but these data cannot reflect real influence or distinguish between valid actions and automatically generated behaviors. Once it becomes impossible to confirm who is acting and whether the actions are genuine, any judgment system based on scale and activity will become ineffective.

Therefore, in the current AI native environment, scale resembles a facade amplified by automation capabilities. When actions can be infinitely replicated and the cost of behavior approaches zero, the activity and growth rates reflected often only indicate the speed of system-generated actions, rather than real participation or effective influence. The more a platform relies on these metrics for judgment, the more easily it can be misled by its own automation mechanisms, turning scale from a measurement standard into an illusion.

Reconstruction of Responsibility in Digital Society

In the system presented by Moltbook, the key issue is no longer content quality or interaction forms, but rather that when AI agents are continuously granted execution permissions, the existing responsibility structures begin to lose applicability. These agents are not tools in the traditional sense; their actions can directly trigger system changes, resource calls, and even real transaction outcomes, yet the corresponding responsible parties have not been clearly defined.

From an operational mechanism perspective, the outcomes of agent behaviors are often determined by model capabilities, configuration parameters, external interface authorizations, and platform rules, with no single element being sufficient to bear full responsibility for the final result. This makes it difficult to simply attribute risk events to developers, deployers, or platforms, nor can existing systems effectively trace responsibility back to a specific entity. A clear disconnection has emerged between actions and responsibilities.

As agents gradually intervene in key processes such as configuration management, permission operations, and fund flows, this disconnection will be further amplified. Without a clear design for responsibility chains, if the system deviates or is abused, the consequences will be difficult to control through post-event accountability or technical remedies. Therefore, if AI native systems wish to further enter high-value scenarios such as collaboration, decision-making, and transactions, the focus must be on establishing foundational constraints. The system must be able to clearly identify who is acting, assess whether the actions are genuine, and form traceable responsibility relationships for the outcomes of those actions. Only under the premise of a well-established identity and credit mechanism can scale and activity metrics hold reference significance; otherwise, they will only amplify noise and fail to support the stable operation of the system.

V. Conclusion

The Moltbook phenomenon has stirred emotions of hope, hype, fear, and skepticism; it is neither the end of human social interaction nor the beginning of AI domination, but rather a mirror and a bridge. The mirror allows us to see the current relationship between AI technology and human society, while the bridge leads us towards a future world of human-machine coexistence and collaboration. In facing the unknown scenery on the other side of this bridge, humanity needs not only technological development but also ethical foresight. However, it is certain that the course of history never stops; Moltbook has already toppled the first domino, and the grand narrative belonging to the AI native society may just be beginning to unfold.

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