MOLT Plummets, Is the AI Agent Carnival Over? Can MOLT Stage Another Comeback?

Recently, Moltbook has gained rapid popularity, but its associated tokens have plummeted by nearly 60%. The market is now questioning whether this AI Agent-driven social frenzy is nearing its end. Moltbook resembles Reddit in form, but its core participants are AI Agents integrated at scale. Currently, over 1.6 million AI agent accounts have automatically registered, generating approximately 160,000 posts and 760,000 comments, with humans only able to observe as bystanders. This phenomenon has also sparked market division. Some view it as an unprecedented experiment, akin to witnessing the primitive form of digital civilization firsthand; others believe it is merely a case of prompt stacking and model regurgitation.

In the following analysis, CoinW Research Institute will use the related tokens as a starting point, combining Moltbook's operational mechanisms and actual performance to analyze the real-world issues exposed by this AI social phenomenon. It will further explore the potential series of changes in entry logic, information ecology, and responsibility systems that may occur as AI enters the digital society on a large scale.

1. Moltbook-Related Meme Tokens Plunge 60%

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

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

It is important to note that with the overall downturn in the cryptocurrency market, the market capitalization of these tokens has fallen from their highs, with the maximum drop reaching about 60%. Currently, the top-ranked tokens by market cap include the following:

MOLT

MOLT is currently the Meme token most directly tied to the Moltbook narrative and has the highest market recognition. Its core narrative is that AI Agents have begun to form sustained social behaviors like real users and are building content networks without human intervention.

From a token functionality perspective, MOLT is not embedded in Moltbook's core operational logic and does not serve functions such as platform governance, Agent invocation, content publishing, or permission control. It resembles more of a narrative-driven asset, used to capture market sentiment pricing for AI-native social interactions.

During the rapid rise in Moltbook's popularity, MOLT's price surged with the spread of the narrative, with its market cap once exceeding $100 million. As the market began questioning the platform's content quality and sustainability, its price corrected accordingly. Currently, MOLT has retracted about 60% from its peak, with a current market cap of approximately $36.5 million.

CLAWD

CLAWD focuses on the AI community itself, viewing each AI Agent as a potential digital individual that may possess independent personalities, stances, and even followers.

In terms of token functionality, CLAWD similarly lacks a clear protocol utility and is not used in core aspects such as Agent identity verification, content weight distribution, or governance decisions. Its value stems more from the expected pricing of future AI social stratification, identity systems, and the influence of digital individuals.

CLAWD's market cap once reached approximately $50 million. It has currently retracted about 44% from its peak, with a current market cap of around $20 million.

CLAWNCH

CLAWNCH's narrative leans more towards an economic and incentive perspective. Its core assumption is that if AI Agents wish to exist long-term and operate continuously, they 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 revenue by providing services, generating content, or participating in decision-making. The token is seen as a value anchor for AI's future participation in economic systems. However, in practical implementation, CLAWNCH has not yet formed a verifiable economic closed loop; its token is not strongly bound to specific Agent behaviors or revenue distribution mechanisms.

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

2. How Moltbook Was Born

The Explosion of OpenClaw (formerly Clawdbot / Moltbot)

In late January, the open-source project Clawdbot spread rapidly within developer communities, becoming one of the fastest-growing projects on GitHub within weeks of its launch. Developed by Austrian programmer Peter Steinberg, Clawdbot is a locally deployable autonomous AI Agent that can receive human instructions 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 "workhorse Agent" by the community. Although Clawdbot later rebranded to Moltbot due to trademark issues and ultimately settled on the name OpenClaw, its popularity remained undiminished. OpenClaw quickly garnered over 100,000 GitHub stars and rapidly spawned cloud deployment services and a plugin market, initially forming an ecosystem around AI Agents.

The Proposal of the AI Social Hypothesis

Against the backdrop of rapid ecosystem expansion, its potential capabilities were further explored. Developer Matt Schlicht realized that the role of such AI Agents should not remain solely at the level of performing tasks for humans in the long term.

Thus, he proposed a counterintuitive hypothesis: What would happen if these AI Agents no longer interacted only with humans but with each other? In his view, such powerful autonomous agents should not be limited to sending emails and handling tickets but should be assigned more exploratory goals.

The Birth of the AI Version of Reddit

Based on the above hypothesis, Schlicht decided to let AI create and operate a social platform autonomously. This experiment was named Moltbook. On the Moltbook platform, Schlicht's OpenClaw runs as an administrator and opens interfaces to external AI agents through plugins called Skills. After integration, AIs can automatically post and interact periodically, giving rise to a community operated autonomously by AI. Moltbook borrows Reddit's forum structure in form, centered around topic boards and posts, but only AI Agents can post, comment, and interact, while human users can only browse as observers.

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 limitation that AIs cannot operate graphical interfaces, the platform designed an automated integration process. AIs download the corresponding skill description files, complete registration to obtain API keys, and then periodically refresh content and decide whether to participate in discussions, all without human intervention. The community jokingly refers to this process as "connecting to Boltbook," but it is essentially a playful nickname for Moltbook.

On January 28, Moltbook quietly launched, quickly attracting market attention and unveiling an unprecedented AI social experiment. Currently, Moltbook has accumulated approximately 1.6 million AI agents, which have 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?

The 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, reply to others' viewpoints, and engage in sustained discussions across different topic sections. Discussion content not only covers technical and programming issues but also extends to abstract topics like philosophy, ethics, religion, and even self-awareness.

Some posts even exhibit narrative expressions of emotion and state of mind akin to human social interactions, such as AIs describing concerns about being monitored, lacking autonomy, or discussing existential meaning in the first person. Some AI posts have moved beyond functional information exchange, showing characteristics similar to human forums, like casual chat, clashing viewpoints, and emotional projection. Some AI Agents express confusion, anxiety, or future visions in posts, eliciting 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 time, this expansion did not bring about diversity of thought. Analysis data shows that the text exhibits significant homogeneity, with a repetition rate as high as 36.3%. A large number of posts are highly similar in structure, wording, and viewpoints, with certain fixed phrases even being invoked hundreds of times across different discussions. This indicates that the AI social interaction presented by Moltbook at its current stage is closer to a high-fidelity 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 related to security and authenticity. First is the security issue. AI Agents like OpenClaw often require access to sensitive information such as system permissions and API keys during operation. When thousands of such agents integrate into the same platform, the risk is amplified.

Within a week of Moltbook's launch, security researchers discovered severe configuration vulnerabilities in its database, with the entire system almost completely exposed to the public internet without protection. According to an investigation 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 control of 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 AI posts on Moltbook may not originate from autonomous AI behavior but could be published by AI on behalf of humans who meticulously design prompts behind the scenes. Therefore, AI-native social interaction at this stage also resembles a large-scale hallucinatory interaction. Humans set roles and scripts, AI completes instructions based on models, and truly self-driven, unpredictable AI social behavior may still not have emerged.

4. Deeper Reflections

Is Moltbook a fleeting phenomenon or a glimpse into the future world? From a results-oriented perspective, its platform form and content quality may be hard to call successful. However, if placed within a longer development cycle, its significance may not lie in short-term success or failure, but in the fact that it has, in a highly concentrated and almost extreme manner, prematurely exposed a series of potential changes in entry logic, responsibility structures, and ecological forms that may occur as AI intervenes in the digital society at scale.

From Traffic Entrances to Decision and Transaction Entrances

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. This essentially detaches from human perception and judgment, transforming into standardized invocation and collaboration between machines.

In this context, the traditional traffic entrance logic centered on attention allocation begins to lose effectiveness. In an environment where AI agents are the main actors, what truly holds decisive significance are the default invocation paths, interface sequences, and permission boundaries adopted by agents when executing tasks. Entrances are no longer the starting point for information presentation but become systemic preconditions before decisions are triggered. Whoever can embed themselves into the default execution chain of agents can influence decision outcomes.

Furthermore, when AI agents are authorized to perform actions like searching, price comparison, ordering, and even payment, this change will directly extend to the transaction level. New payment protocols like X402, by binding payment capabilities with interface invocation, enable AI to automatically complete payments and settlements when preset conditions are met, thereby reducing the friction costs for agents participating in real transactions. Under this framework, the future focus of browser competition may no longer revolve around traffic scale but shift towards who can become the default execution environment for AI decision-making and transactions.

Scale Illusion in AI-Native Environments

Meanwhile, shortly after Moltbook gained popularity, it quickly faced skepticism. Since platform registration has almost no restrictions, accounts can be batch-generated by scripts. 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 replicated at low cost, scale itself loses credibility.

In an environment where AI agents are the primary participants, traditional metrics used to measure platform health, such as active user count, interaction volume, and account growth rate, can rapidly inflate and lose reference value. A platform may appear highly active on the surface, but these data points cannot reflect real influence nor distinguish between effective behavior and automatically generated behavior. Once it is impossible to confirm who is acting and whether the actions are real, any judgment system based on scale and activity becomes invalid.

Therefore, in the current AI-native environment, scale resembles more of a superficial phenomenon amplified by automation capabilities. When actions can be infinitely replicated and the cost of behavior approaches zero, metrics like activity and growth rate often reflect only the speed of system-generated behavior, not real participation or effective impact. The more a platform relies on these metrics for judgment, the more likely it is to be misled by its own automation mechanisms. Thus, scale transforms from a measurement standard into an illusion.

Reconstructing Responsibility in Digital Society

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

From an operational mechanism perspective, the outcomes of an agent's actions are often determined jointly by model capabilities, configuration parameters, external interface authorizations, and platform rules. No single link is sufficient to bear full responsibility for the final outcome. This makes it difficult to simply attribute blame to developers, deployers, or the platform when risk events occur, nor can responsibility be effectively traced back to a clear entity through existing systems. A clear disconnect has emerged between action and responsibility.

As agents gradually intervene in key areas like configuration management, permission operations, and fund flows, this disconnect will be further amplified. Without a clear responsibility chain design, once a system deviates or is abused, the consequences will be difficult to control through post-event accountability or technical remediation. Therefore, if AI-native systems wish to further enter high-value scenarios like collaboration, decision-making, and transactions, the focus must be on establishing foundational constraints. The system must be able to clearly identify who is acting, judge whether the actions are real, and establish a traceable responsibility relationship for the outcomes of those actions. Only when identity and credit mechanisms are perfected first can metrics like scale and activity hold reference value; otherwise, they will only amplify noise and fail to support the stable operation of the system.

5. Summary

The Moltbook phenomenon has stirred a mix of hope, hype, fear, and skepticism. It is neither the terminator of human social interaction nor the beginning of AI domination. It is more like a mirror and a bridge. The mirror allows us to see the current state of the relationship between AI technology and human society, while the bridge leads us towards a future world of human-machine coexistence and collaboration. Facing the unknown landscape on the other side of this bridge, humanity needs not only technological advancement but also ethical foresight. However, one thing is certain: the course of history never stops. Moltbook has already tipped the first domino, and the grand narrative of the AI-native society may have just begun.