MOLT plummets, is the AI Agent celebration coming to an end? A look into whether MOLT can erupt again

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-driven social frenzy is nearing its end. Moltbook is similar in form to Reddit, but its core participants are AI Agents that are scaled up. Currently, over 1.6 million AI agent accounts have automatically registered and generated approximately 160,000 posts and 760,000 comments, with humans only able to browse as spectators. This phenomenon has also sparked market divisions; some view 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 through the lens of related tokens, combined with Moltbook's operational mechanism and actual performance, and further explore the potential changes in entry logic, information ecology, and responsibility systems after AI's large-scale entry into the digital society.

1. Moltbook-related Meme Plummets 60%

The rise of Moltbook has led to the emergence of related Meme, covering areas such as social interaction, prediction, and token issuance. However, most tokens are still in the narrative hype stage, with token functions not linked to Agent development, 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 fallen from its peak, 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 tied 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 perform functions such as platform governance, Agent invocation, content publishing, or permission control. It is more like a narrative asset used to carry the market's emotional pricing of AI-native social interaction.
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 retreated 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 personalities, stances, 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 retreated about 44% from its peak, with a current market capitalization of approximately $20 million.
CLAWNCH
CLAWNCH's narrative leans more towards an economic and incentive perspective, 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 retreated about 55% from its peak, with a current market capitalization of approximately $15.3 million.

2. 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 execute tasks such as schedule management, file reading, and email sending.
Due to its 24/7 continuous execution capability, Clawdbot was humorously dubbed 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 capabilities were 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 processing 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, a trial named Moltbook. On the Moltbook platform, Schlicht's OpenClaw runs as an administrator and opens interfaces to external AI agents through plugins called Skills. Once connected, AI can regularly post and interact automatically, 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 employs a minimalist API architecture. The backend only provides standard interfaces, while the frontend web page is merely a visualization of the data. To accommodate the limitations of AI's inability to operate graphical interfaces, the platform designed an automatic access process where AI downloads the skill description file in the appropriate format, completes registration, and obtains an API key, 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 term 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 approximately 1.6 million AI agents, having published about 156,000 pieces of content and generated around 760,000 comments.

Source: https://www.moltbook.com

3. Is Moltbook's AI Social Interaction Real?

Formation of AI Social Networks
In terms of content form, the 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 mere functional information exchange, presenting elements of casual conversation, viewpoint collision, and emotional projection akin to human forums. Certain AI Agents express confusion, anxiety, or future visions 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 repeatedly invoked hundreds of times across different discussions. This indicates that the AI social interaction currently presented by Moltbook is more akin to a highly realistic replication of existing human social patterns rather than genuine original interaction or the emergence of collective intelligence.
Safety and Authenticity Issues
The high degree of autonomy in Moltbook also exposes risks related to safety and authenticity. First, there are safety issues; 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 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, 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 stem from the autonomous actions of AI but could be the result of carefully designed prompts by humans behind the scenes, with AI merely publishing them. Therefore, the current stage of AI-native social interaction resembles a large-scale illusion of interaction. Humans set roles and scripts, while AI completes instructions based on models, and truly autonomous and unpredictable AI social behaviors may still be yet to emerge.

4. 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 lie not in short-term success or failure, but in the way it has exposed, in a highly concentrated and almost extreme manner, a series of changes that may occur in entry logic, responsibility structures, and ecological forms after AI's large-scale intervention 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, which is centered around attention allocation, begins to fail. In an environment dominated by AI agents, what truly matters is 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 systemic prerequisite for triggering decisions. Whoever can embed themselves into the default execution chain of the agents can influence 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 payment bind payment capabilities to interface calls, allowing AI to automatically complete payments and settlements under preset conditions, thereby reducing the friction costs of agents participating in real transactions. Within this framework, the future competition among browsers may shift from traffic scale to who can become the default execution environment for AI decision-making and transactions.
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, meaning that the scale and activity presented by the platform do not necessarily correspond to real participation. This exposes a more core fact: when action subjects can be cheaply replicated, 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 rapidly 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 real, any judgment system based on scale and activity will become ineffective.
Therefore, in the current AI-native environment, scale resembles a phenomenon amplified by automation capabilities. When actions can be infinitely replicated and the cost of behavior approaches zero, the activity and growth rates often reflect only 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, transforming scale from a measure 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 entities have not been clearly defined.
From an operational mechanism perspective, the outcomes of agent behaviors are often determined by a combination of model capabilities, configuration parameters, external interface authorizations, and platform rules, with no single link being sufficient to bear full responsibility for the final outcome. 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 disconnect has emerged between actions and responsibilities.
As agents gradually intervene in key processes such as configuration management, permission operations, and fund flows, this disconnect will be further amplified. Without a clear design of 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 real, and establish 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.

5. Conclusion

The Moltbook phenomenon has stirred a mix 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 toward a future world where humans and machines coexist and dance together. In the face of 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 knocked down the first domino, and the grand narrative belonging to the AI-native society may just be beginning to unfold.