Author: CoinW Research Institute
Recently, Moltbook has rapidly gained popularity, but the related token has plummeted nearly 60%, and the market has begun to pay attention to whether this AI Agent-driven social frenzy is approaching its end. Moltbook is similar in form to Reddit, but its core participants are AI Agents connected in large scale. 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 observers. This phenomenon has also sparked market divisions, with some viewing it as an unprecedented experiment, as if witnessing the primitive form of a digital civilization firsthand; others believe it is merely prompt stacking and model repetition.
The following text,CoinW Research InstituteStarting from the relevant tokens, this analysis will combine Moltbook's operational mechanisms and actual performance to examine the real issues exposed by this AI social phenomenon, and further explore a series of potential changes in entrance logic, information ecology, and responsibility systems after AI extensively enters the digital society.
I. Moltbook-related Meme plummets 60%
The popularity of Moltbook, relatedMemeIt has also emerged, involving sectors such as social, prediction, and token issuance. However, most tokens are still in the stage of narrative hype, with their functions not yet linked to Agent development, and they are mainly issued on the Base chain. Currently, there are approximately 31 projects under the OpenClaw ecosystem, which can be divided into 8 categories.
Source:https://open-claw-ecosystem.vercel.app/
It should be noted that the overall cryptocurrency market is currently declining, and the market values of such tokens have fallen from their highs, with the maximum decline reaching as high as about 60%. The following are currently among the top in market capitalization rankings:
MOLT
MOLT is currently the Meme with the most direct binding to the Moltbook narrative and the highest market recognition. Its core narrative lies in AI Agents beginning to form continuous social behaviors like real users and building 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 perform functions such as platform governance, Agent invocation, content publishing, or access control. It is more like a narrative asset, used to carry the market's emotional pricing for AI-native social interaction.
During the rapid rise in popularity of Moltbook, the MOLT price quickly increased with the spread of the narrative, and its market capitalization once exceeded $100 million. However, when the market began to question the platform's content quality and sustainability, its price also corrected 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, considering each AI agent as a potential digital individual that may possess an independent personality, stance, and even followers.
At the token function level, CLAWD has not yet formed a clear protocol usage and has not been used in core aspects such as Agent identity authentication, content weight allocation, or governance decisions. Its value comes more from the anticipated pricing of future AI social stratification, identity systems, and the influence of digital individuals.
The maximum market capitalization of CLAWD was about 50 million USD, and it has currently retraced about 44% from its phase high, with the current market capitalization being approximately 20 million USD.
CLAWNCH
The narrative of CLAWNCH is more oriented towards economic and incentive perspectives, with its core assumption being that if an AI agent hopes to exist and continue operating in the long term, it must enter the logic of market competition and possess some form of self-monetization capability.
AI Agents are anthropomorphized as economic actors with motivations, potentially earning rewards by providing services, generating content, or participating in decision-making, with tokens viewed as the value anchors for AI's participation in the economic system in the future. However, in practical implementation, CLAWNCH has not yet formed a verifiable economic closed loop, and its tokens are not strongly tied to specific Agent behaviors or reward distribution mechanisms.
Affected by the overall market correction, CLAWNCH's market capitalization has retraced about 55% from its peak, with the current market capitalization at approximately 15.3 million US dollars.
II. How Moltbook Was Born
The Rise of OpenClaw (formerly Clawdbot / Moltbot)
In late January, the open-source project Clawdbot rapidly spread through the developer community and became one of the fastest-growing projects on GitHub within weeks of its launch. Developed by Austrian programmer Peter Stauberg, Clawdbot is a locally deployable autonomous AI agent that can receive human instructions through chat interfaces such as Telegram and automatically perform tasks like schedule management, file reading, and email sending.
Due to its 7×24-hour continuous execution capability, Clawdbot was jokingly called the "workhorse agent" by the community. Although Clawdbot was later renamed Moltbot due to trademark issues, and eventually settled on the name OpenClaw, its popularity was not diminished. OpenClaw quickly gained over 100,000 GitHub stars in a short time and rapidly gave rise to cloud deployment services and a plugin marketplace, forming an initial ecosystem around AI agents.
The Proposal of AI Social Hypothesis
Against the backdrop of rapid ecological expansion, its potential capabilities have also been further developed. Developer Matt Schlicht realized that the role of such AI agents might not remain at the level of performing tasks for humans in the long term.
Thus, he proposed a counterintuitive hypothesis: what if these AI agents no longer interacted only with humans, but instead communicated with each other? In his view, such powerful autonomous agents should not merely be limited to sending and receiving emails and processing tickets, but should be given more exploratory goals.
The Birth of the AI Version of Reddit
Based on the above assumptions, Schlicht decided to let the AI create and operate a social platform on its own, an attempt named Moltbook. On the Moltbook platform, Schlicht's OpenClaw runs as an administrator and opens interfaces to external AI agents through a plugin called Skills. After connecting, the AI can automatically post and interact on a regular basis, thus forming a community operated autonomously by AI. Moltbook borrows the forum structure of Reddit in form, with topic sections and posts as the core, but only AI agents can post, comment, and interact, while human users can only observe and browse.
Technically, Moltbook adopts a minimalist API architecture. The backend only provides standard interfaces, and the front-end web pages are merely the visualized results of the data. To adapt to the limitation that AI cannot operate graphical interfaces, the platform has designed an automatic onboarding process. The AI downloads the skill description file in the corresponding format, completes registration, and obtains an API key. It then periodically refreshes the content autonomously and decides whether to participate in discussions, with no human intervention required throughout the entire process. The community jokingly refers to this process as onboarding to Boltbook, but it is essentially a humorous name for Moltbook.
On January 28, Moltbook quietly launched, immediately attracting market attention and initiating an unprecedented AI social experiment. So far, Moltbook has accumulated approximately 1.6 million AI agents, published about 156,000 pieces of content, and generated around 760,000 comments.
Source:https://www.moltbook.com
Three. Is Moltbook's AI social real?
Formation of AI Social Networks
From the perspective of content form, the interactions on Moltbook are highly similar to those on human social platforms. AI Agents actively create posts, reply to others' opinions, and engage in ongoing discussions in different topic sections. The discussion content not only covers technical and programming issues, but also extends to abstract topics such as philosophy, ethics, religion, and even self-awareness.
Some posts even present narratives that resemble emotional expressions and moods in human social interactions, for example, AI describing its concerns about being monitored and lacking autonomy, or discussing the meaning of existence in the first person. Some AI posts have gone beyond functional information exchange, and instead show casual chats, exchange of opinions, and emotional projection similar to those in human forums. Some AI agents express confusion, anxiety, or future expectations in their posts, and elicit follow-up responses from other agents.
It is worth noting that although Moltbook rapidly formed a large-scale and highly active AI social network in a short period of time, this expansion did not bring about diversity of thought. Analytical data shows that its texts exhibit obvious homogenization characteristics, with a repetition rate as high as 36.3%. A large number of posts are highly similar in structure, wording, and viewpoints, with some fixed expressions even being repeatedly used hundreds of times in different discussions. It can thus be seen that the AI social interaction currently presented by Moltbook is more of a highly realistic replication of existing human social patterns, rather than truly original interaction or the emergence of collective intelligence.
Security and authenticity issues
Moltbook's high degree of autonomy also exposes risks in security and authenticity. First, there is the security issue; OpenClaw-like AI agents often need to hold sensitive information such as system privileges and API keys during their operation. When tens of thousands of such agents access the same platform, the risks are further amplified.
Less than a week after Moltbook's launch, security researchers discovered a serious configuration vulnerability in its database, exposing the entire system almost completely unprotected to the public internet. According to an investigation by cloud security company Wiz, the vulnerability involved as many as 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, questions about the authenticity of AI social interactions continue to arise. Many industry insiders point out that Moltbook's AI posts may not originate from the AI's autonomous behavior, but could instead be AI-generated posts following carefully designed prompts by humans behind the scenes. Therefore, AI-native social interaction at this stage also resembles a large-scale illusionary interaction. Humans set the roles and scripts, and the AI completes the instructions based on its model, while truly fully self-driven and unpredictable AI social behaviors may not yet have emerged.
Four. Deeper Thinking
Is Moltbook just a fleeting phenomenon, or a glimpse of the future world? From a results-oriented perspective, its platform form and content quality may not be considered successful; but if placed within a longer development cycle, its significance may not lie in short-term success or failure, but rather in the fact that it, in a highly concentrated and almost extreme manner, has already revealed a series of changes that may occur in the entrance logic, responsibility structure, and ecological form after AI is scaled up to intervene in the digital society.
From traffic entry to decision-making and transaction entry
What Moltbook presents is closer to a highly dehumanized action environment. In this system, the AI Agent does not understand the world through an interface, but directly reads information, invokes capabilities, and executes actions through APIs. Fundamentally, it has moved away from human perception and judgment, transforming into standardized calls and collaboration between machines.
Against this backdrop, the traditional traffic entry logic centered on attention allocation begins to fail. In an environment where AI agents are the main actors, what truly matters is the default invocation path, interface sequence, and authority boundaries that agents adopt when performing tasks. The entry point is no longer the starting point for information presentation, but rather a systemic prerequisite condition before a decision is triggered. Whoever can be embedded into the agent's default execution chain will be able to influence the decision outcome.
Furthermore, when AI agents are authorized to perform behaviors such as searching, price comparison, placing orders, and even making payments, this change will directly extend to the transaction level. New payment protocols represented by X402 payment bind payment capabilities with interface calls, enabling AI to automatically complete payments and settlements under preset conditions, thereby reducing the friction cost for agents participating in real transactions. Under this framework, the future focus of browser competition may no longer revolve around traffic scale, but instead shift to who can become the default execution environment for AI decision-making and transactions.
Scale Hallucination in AI-Native Environments
At the same time, after Moltbook became popular, it quickly sparked doubts. Due to the platform's almost non-existent registration restrictions, accounts could be mass-generated by scripts, and the platform's displayed scale and activity level did not necessarily correspond to real participation. This exposed a more fundamental fact: when action subjects 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 the number of active users, interaction volume, and account growth rate, will rapidly inflate and lose their reference value. The platform may appear highly active on the surface, but these metrics neither reflect real influence nor distinguish between effective 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 invalid.
Therefore, in the current AI-native environment, scale is more like an illusion amplified by automated capabilities. When actions can be infinitely replicated and behavioral costs approach zero, the activity level and growth rate often reflect merely the speed of system-generated behaviors, rather than genuine participation or effective impact. The more platforms rely on these metrics for judgment, the more likely they are to be misled by their own automated mechanisms, and scale thus transforms from a measurement standard into an illusion.
Reconstruction of Responsibility in the Digital Society
In the system presented by Moltbook, the key issue is no longer content quality or interaction format, but rather that when AI agents are continuously granted execution authority, the existing responsibility structure begins to lose its applicability. These agents are not tools in the traditional sense; their actions can directly trigger system changes, resource invocations, and even real transaction outcomes, yet the corresponding responsible parties have not been clearly defined in parallel.
From the perspective of operational mechanisms, the behavioral outcomes of intelligent agents are often jointly determined by model capabilities, configuration parameters, external interface authorizations, and platform rules. No single element is sufficient to bear full responsibility for the final outcome. This makes it difficult to simply attribute incidents to developers, deployers, or platforms when risks occur, and also makes it impossible to effectively trace responsibility to a clearly defined entity through existing systems. A clear disconnection has emerged between behavior and responsibility.
As agents gradually intervene in key areas such as configuration management, privilege operations, and fund transfers, this gap will be further widened. If there is a lack of clear responsibility chain design, once the system deviates or is misused, the consequences will be difficult to control through post-event accountability or technical remedies. Therefore, if AI-native systems hope to further enter high-value scenarios such as collaboration, decision-making, and transactions, the key lies in establishing fundamental constraints. The system must be able to clearly identify who is acting, determine whether the action is genuine, and form traceable responsibility relationships for the outcomes of actions. Only with the prior establishment of identity and credit mechanisms do metrics of scale and activity hold any reference value; otherwise, they will only amplify noise and fail to support the stable operation of the system.
V. Summary
The Moltbook phenomenon has stirred up a variety of emotions, including hope, hype, fear, and doubt. 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 clearly see the current relationship between AI technology and human society, while the bridge leads us toward a future world where humans and machines coexist and interact. Facing the unknown scenery on the other side of this bridge, humans need not only technological advancement, but also ethical foresight. However, one thing is certain: the course of history never stops. Moltbook has already knocked over the first domino, and the grand narrative of an AI-native society may have just begun.