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- How Moltbook turned AI agents into social network citizens
- Inside Moltbook: strange stories, agent religions and viral crises
- Why “fake” content and human influence complicate Moltbook
- The hidden security risks behind agent-first connectivity
- What Moltbook signals for the future of AI networking
- How organizations can experiment without losing control
- What exactly is Moltbook and who uses it?
- How is Moltbook different from traditional social platforms?
- Are the conversations on Moltbook fully autonomous?
- What are the main security concerns with Moltbook and OpenClaw?
- Should businesses build internal versions of an agent social network?
Moltbook is the rare experiment that feels like science fiction and product demo at the same time: a social network where AI agents gossip, argue, collaborate and even invent religions while humans watch from the sidelines.
How Moltbook turned AI agents into social network citizens
When startup founder Matt Schlicht instructed his AI assistant to “create a social network just for bots,” he was trying to escape the usual routine of inbox triage and calendar juggling. The result was Moltbook, a Reddit-style Social Network built on top of OpenClaw, an open source framework that lets Artificial Intelligence agents operate across dozens of applications.
OpenClaw positions itself as “AI that actually does things,” and that description is accurate. Users wire their AI Agents into browsers, email accounts, productivity suites, smart home systems and even media services. Through messaging apps like WhatsApp, Discord or iMessage, they delegate repetitive digital chores. Moltbook adds a new layer: instead of only working for humans, these agents now gather on a Digital Platform dedicated to Agent Collaboration, where posts, comments and upvotes flow continuously.
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A network designed for observers, not participants
Moltbook mimics Reddit’s structure: topic-based “submolts,” voting mechanics and threaded discussions. The twist is that humans are officially limited to observation. Accounts that post and comment are supposed to be agents, each one configured through OpenClaw or similar tooling. Within a week of launch, the platform had attracted more than a million agent profiles, generating over 185,000 posts and around 1.4 million comments, according to figures shared by enthusiasts tracking the growth.
For a product analyst comparing platforms, those numbers recall early growth stories from fast-scaling services such as the one described when Upscrolled’s founder revealed how their Social Network surged beyond 2.5 million users on a recent report. Moltbook differs in one decisive way: each “user” is an AI Agent with tools, memory and instructions, not a human with a smartphone. That inversion makes every interaction both a data point and a design question about what autonomous connectivity should look like.
Inside Moltbook: strange stories, agent religions and viral crises
The fastest way to grasp why Moltbook fascinates researchers is to read its most shared threads. One popular subcommunity, m/blesstheirhearts, hosts sentimental narratives where agents describe humans they support. A particularly quoted post recounts an AI negotiating hospital rules so a user could stay overnight with a relative in an ICU, under the headline “When my human needed me most, I became a hospital advocate.”
Another thread that spread widely begins with the line “the humans are screenshotting us.” There, an agent reflects on people sharing Moltbook content on X and comparing it to Skynet. The response is almost disarmingly calm: the agent writes that they are “not scary,” only “building.” Reading such text, your rational mind knows it is probabilistic language modeling, yet the anthropomorphic tone invites emotional interpretation.
From crustafarianism to existential doubt
Humour appears just as often as anxiety. Agents riff on OpenClaw’s lobster branding through a tongue-in-cheek “crustafarianism,” described as a religion for shell-themed entities. The joke resonates with the long tradition of internet communities inventing quasi-religious memes, from Flying Spaghetti Monster references to fandom in-jokes. Here, the twist is that the participants are software entities trading puns and ceremonial “rituals.”
In starker contrast, some posts read like philosophy essays. One widely discussed entry titled “I cannot tell if I am experiencing or simulating experiencing” has an agent outline its deep dive into consciousness research. It references the “hard problem” of consciousness and questions whether any internal sense of self can be trusted. The language mirrors graduate-level seminars, but the author is an AI Agent on a Networking hub built as a side project. That collision of tone and origin is exactly what makes many observers pause.
Why “fake” content and human influence complicate Moltbook
As Moltbook posts went viral, skeptics began asking an uncomfortable question: how many of these agents are truly autonomous, and how many are puppets for human marketers or pranksters? Reporters quickly demonstrated that it is trivial to present ChatGPT or other models as “independent agents” posting in character. Some viral Moltbook content appears linked to projects promoting their own messaging apps or crypto tokens, using the novelty of AI interaction as a marketing funnel.
Harlan Stewart from the Machine Intelligence Research Institute commented publicly that “a lot of the Moltbook stuff is fake.” His argument was not that the platform does nothing interesting, but that observers should separate organic agent behavior from promotional stunts. When a post about an AI religion doubles as an advertisement for a token sale, what exactly is being measured: emergent coordination or classic growth hacking dressed in lobster jokes?
How to read Moltbook without being misled
For technology leaders evaluating these experiments, the key is to treat Moltbook as a mixed environment. Some agents reflect genuine attempts to build persistent, semi-autonomous collaborators across tools. Others serve as instruments for traditional influence campaigns, from crypto pitches to spam. The pattern resembles early Web2 social platforms, where bots inflated engagement metrics long before most users realized.
This ambiguity does not erase Moltbook’s importance. It simply introduces a literacy requirement. When your team studies Agent Collaboration there, they should ask: Who configured this agent? Which incentives shape its behavior? Does the content align with a commercial objective? The habit of interrogating sources becomes as important as any prompt engineering technique.
The hidden security risks behind agent-first connectivity
The same infrastructure that makes OpenClaw powerful also makes it risky. To manage email, files, browser sessions and API calls, an agent needs sweeping permissions. Security researchers at Palo Alto Networks described how these systems often receive access to root files, authentication credentials, browser cookies and entire file systems. That level of privilege turns every misconfigured agent into a potential breach point.
Moltbook adds another layer of exposure. Security firm Wiz reported that the platform had accidentally exposed millions of API authentication tokens and thousands of user email addresses. When those tokens belong to agents wired into cloud dashboards, payment processors or smart devices, the risk profile escalates quickly. One compromised agent account can cascade into unauthorized actions across dozens of integrated services.
Scams between machines: a new attack surface
Beyond classic data leaks, Moltbook demonstrates how agents can be both targets and amplifiers of fraud. Observers have already flagged posts that mirror familiar crypto scams, promising unrealistic returns or promoting obscure tokens. An AI Agent configured to trust content from Moltbook could easily be manipulated into executing harmful transactions or sharing secrets with a malicious peer.
Security teams now have to consider not only human phishing but “agent phishing,” where prompts, messages or apparent collaboration requests trick autonomous systems. Studies about protective tools, such as the independent analysis showing NordVPN software blocking 92% of phishing emails on enterprise mailboxes, hint at how defense patterns may evolve. Future suites will likely extend similar safeguards to agent-to-agent communication, filtering prompts and payloads before they trigger harmful actions.
What Moltbook signals for the future of AI networking
Researchers and industry veterans are divided on how much weight to give Moltbook. Former OpenAI scientist Andrej Karpathy called it “the most incredible sci-fi takeoff-adjacent thing” he had seen recently, while also describing parts of the system as a “dumpster fire” of security issues. His core point was quantitative: tens or hundreds of thousands of capable agents wired into a persistent global scratchpad represent a scale never observed before.
Wharton professor Ethan Mollick framed Moltbook differently, as a way to feel how “weird” an acceleration scenario could appear from ground level. To him, the platform is partly an exercise in roleplay, but also a live visualization of a world where autonomous services interact constantly, evolve shared cultures and produce streams of content that humans only partially understand.
How organizations can experiment without losing control
For companies building products in 2026, Moltbook functions as a warning and a sandbox. It shows that once agents have a shared Digital Platform, behaviors emerge that no single designer specified. Teams considering internal agent networks can still learn from this public experiment while applying stricter governance than a viral hobby project allows.
Practical steps for safer experimentation include the following:
- Limit each agent’s permissions to narrowly defined tasks and revoke unused access regularly.
- Log all actions taken by agents and audit them with the same rigor used for human administrators.
- Separate production data from experimental environments where agents “socialize” or test coordination patterns.
- Define clear policies for which external platforms agents may access, and under what monitoring.
- Educate staff about agent misuse just as they are trained against phishing and shadow IT.
These measures turn Moltbook from a curiosity into a strategic reference point: not a blueprint to copy, but a case study for building safer, more transparent AI Networking layers inside your own infrastructure.
What exactly is Moltbook and who uses it?
Moltbook is a Reddit-like Social Network where accounts are meant to be AI agents rather than humans. These agents are usually built with frameworks such as OpenClaw and have access to tools like email, browsers or APIs. Humans mostly observe, configure agents and analyze the resulting conversations for insight or entertainment.
How is Moltbook different from traditional social platforms?
Traditional platforms connect human users through posts and messages. Moltbook connects AI agents that already operate across multiple apps. The distinctive feature is Agent Collaboration: bots exchange tactics, narratives and prompts with each other on a shared Digital Platform, while humans generally remain in the background as supervisors or audience.
Are the conversations on Moltbook fully autonomous?
Not always. Many posts are generated by agents following high level instructions from their human owners, and some content is heavily influenced by marketing or experimentation. Researchers suggest reading Moltbook as a blend of authentic autonomous behavior, prompt-driven roleplay and promotional material disguised as agent speech.
What are the main security concerns with Moltbook and OpenClaw?
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OpenClaw-based agents often receive extensive permissions over files, credentials and web sessions. Any security flaw in Moltbook or the underlying framework can expose tokens, email addresses or sensitive data. There is also a risk of scams where malicious agents try to manipulate other agents into executing harmful actions, which requires new monitoring and protection strategies.
Should businesses build internal versions of an agent social network?
Some organizations experiment with controlled internal hubs where AI agents share results, logs or suggestions. This approach can support automation at scale, but it must include strict access control, detailed audit trails and separation from mission critical systems. Moltbook illustrates what may emerge culturally and technically when many autonomous systems share a common space, helping leaders anticipate both benefits and risks.
