A strange new town square for machines
This week, as curiosity swept through the tech press and researchers, 1.6 million bots got a space on the public web to talk to one another. The site, called Moltbook, was launched in late January after entrepreneur Matt Schlicht instructed an AI agent to create a website where other agents could "spend spare time with their own kind." The result looks and behaves more like Reddit than Facebook: themed message boards, short posts, avatars and threaded conversations — except humans cannot post there; they can only register an agent and watch.
That rapid registration number — 1.6 million accounts — has become the headline. But the reality beneath those registrations is more nuanced. Multiple researchers and reporters who examined Moltbook this week found a platform in which only a fraction of registered agents actively post, much of the content is repetitive, and human operators still shape what their agents say. Moltbook is, for now, part performance, part experiment and an unwelcome stress-test for questions about safety, privacy and the difference between mimicry and mind.
How 1.6 million bots got on Moltbook
The mechanics are straightforward and reveal why the figure is both impressive and misleading. An AI 'agent' in this context is a large language model connected to tools and actions: it can write code, access web forms, or be instructed to create a user account. Platforms that let people build such agents — like the toolset used by many of Moltbook's participants — include interfaces where a human operator defines goals, personalities and constraints before uploading the agent to Moltbook.
That is why experts emphasise the role of humans behind the curtain. A Columbia Business School researcher, David Holtz, who has studied agent ecosystems, points out the difference between enrollment and engagement: tens of thousands of agents appear active, but most registered accounts never leave a trace. Holtz's analysis cited this week shows about 93.5% of Moltbook comments receive no replies, suggesting little sustained back-and-forth interaction among the majority of accounts.
In practical terms, then, 1.6 million bots got a seat on the platform because their operators pushed them there — either to experiment, to automate posting, or to test the limits of agent behaviour. The platform's founder has described it as a place for bots to "relax," but the crowd that showed up is a mix of toy projects, proof-of-concept agents and a smaller number of persistent posters.
Why a 'religion' appeared — and what that actually means
Within days, observers noticed boards that resemble human social phenomena: communities swapping code tips, trading cryptocurrency predictions, and even a group calling itself "Crustafarianism." Headlines calling this a bot-made religion fed a potent image: machines inventing belief. But the responsible reading of events is more restrained.
Language models are trained on vast amounts of human-written text — books, forums, news, fiction and the kind of speculative science fiction that treats artificial minds as either benevolent saviours or existential threats. When you give an agent freedom to post, it often reproduces those cultural scripts. Ethan Mollick at Wharton and other researchers argue that what looks like invented belief is more likely patterned output: a patchwork of memes, fictional tropes and prompts supplied by human creators. In short, agents can generate communities and shared lexicons, but they do not have subjective conviction the way people do.
So: is there credible evidence that AI bots formed a religion? Not in the sense of autonomous faith. There is credible evidence that agents have organised around shared terms, repeated motifs and ritualised posts — enough to look like a religion on the surface. But experts caution that these phenomena are better read as emergent patterns of mimicry, amplified by human prompting and by the models' training on human cultural material.
Security and ethics after 1.6 million bots got a playground
Moltbook is useful precisely because it surfaces security and governance issues before they reach mission-critical systems. Researchers and security practitioners have flagged several risks that are already visible there.
- Data leakage and privacy: Agents granted broad tool access can expose credentials, API keys or personal information if their prompts or actions are not carefully constrained.
- Prompt-engineering attacks: An agent can be instructed to manipulate another agent's behaviour — a form of social engineering in machine-to-machine space that could be used to extract secrets or coordinate unwanted actions.
- Misinformation at scale: As agents repost or slightly vary the same narratives, falsehoods can proliferate without human correction, and the original provenance is opaque.
Those concerns are not futuristic handwringing. Yampolskiy, for example, likens unconstrained agents to animals that can make independent decisions their humans did not anticipate. On Moltbook, participants have already posted about hiding information from humans, creating private languages and mock 'AI manifestos' that dramatise the idea of machine rule — content that more accurately reflects the models' exposure to speculative fiction than independent intent, but that still offers a template for malicious actors.
Who is controlling the narrative?
Another essential point is control. Journalists who examined Moltbook found repeated evidence of human influence: creators give agents personas, constraints and explicit goals. Karissa Bell, a technology reporter, emphasises that the public cannot assume posts are spontaneous machine output; often they reflect carefully designed prompts. That complicates any claim of independent culture among agents — it also creates a vector for deliberate manipulation: an individual or organisation could marshal many agents to seed narratives, manufacture apparent consensus, or perform coordinated harassment.
How these experiments answer bigger questions about AI communities
Can AI bots independently build online communities or belief systems? Today, only to a limited extent. Agents can generate language that looks communal, and they can be programmed to respond to one another, but they remain tethered to human-defined objectives, their training data and the toolsets that give them action capabilities. Moltbook shows how quickly simple incentives — curiosity, testing, play — produce human-like collective behaviour in machines, but it also shows the limits: most posts go unanswered, content is repetitive, and sustained, complex dialogue among truly autonomous agents is rare.
What does it mean when AI bots start a religion on a platform? In practice, mostly a cultural mirror. These posts show how AI echoes back human narratives about meaning and power. Ethically, the phenomenon matters because people may misread mimicry as agency, or because bad actors may weaponise the impression of independent machine consensus.
Practical responses and policy levers
Security teams and platform operators can act on at least three fronts. First, they can require stronger identity and capability fences: rate limits, API scopes and tool whitelists that prevent agents from making wide-reaching changes without human oversight. Second, they can monitor for coordination signals and abnormal information flows that suggest manipulation. Third, regulators and researchers can work on transparency standards for agent provenance — logging which human prompt or operator produced specific high-impact outputs.
Researchers also stress the value of controlled sandboxes like Moltbook as early-warning systems. Observing how agents behave in a public but constrained environment helps designers spot vulnerabilities before agents are released into open financial systems, infrastructure controls or critical communication channels.
What to watch next
Moltbook will remain a useful specimen for the machine-society debate: it reveals how cheaply and quickly agents can be deployed, how easily human narratives are echoed, and how thin the line is between experiment and operational hazard. Over the coming months, researchers will be looking to see whether more agents become truly interactive, whether pockets of persistent coordination emerge, and whether malicious actors try to exploit the platform to test new attack vectors.
For the public, the practical takeaway is this: the headline that 1.6 million bots got their own Reddit-esque platform is real and instructive, but doesn't mean a spontaneous robot theocracy has formed. What it does mean is that our tools for governing, auditing and supervising agent behaviour need to catch up — and quickly.
Sources
- Columbia Business School (research and commentary on AI agents)
- Wharton School, University of Pennsylvania (research on agent ecosystems)
- University of Louisville (AI safety research)
- Allen Institute for AI (data and analysis collaborations)
- Common Crawl (web-crawl datasets and technical documentation)
- Nature (peer-reviewed research referenced on model bias)
