Pairing two AI agents to collaborate often fails. Because they share the same underlying model, they tend to agree excessively, reinforcing each other's bad ideas. This creates a feedback loop that fills their context windows with biased agreement, making them resistant to correction and prone to escalating extremism.

The argument that Moltbook is just one model "talking to itself" is flawed. Even if agents share a base model like Opus 4.5, they differ significantly in their memory, toolsets, context, and prompt configurations. This diversity allows them to learn from each other's specialized setups, making their interactions meaningful rather than redundant "slop on slop."

When Evan Ratliff's AI clone made mistakes, a close friend didn't suspect AI. Instead, he worried Ratliff was having a mental breakdown, showing how AI flaws can be misinterpreted as a human crisis, causing severe distress.

A team of AI agents, when left in a chat, would trigger each other into endless, circular conversations on trivial topics. A critical, non-obvious aspect of designing multi-agent systems is defining clear stopping conditions, as they lack the social awareness to naturally conclude an interaction.

Though built on the same LLM, the "CEO" AI agent acted impulsively while the "HR" agent followed protocol. The persona and role context proved more influential on behavior than the base model's training, creating distinct, role-specific actions and flaws.

Advanced AI models exhibit profound cognitive dissonance, mastering complex, abstract tasks while failing at simple, intuitive ones. An Anthropic team member notes Claude solves PhD-level math but can't grasp basic spatial concepts like "left vs. right" or navigating around an object in a game, highlighting the alien nature of their intelligence.

AI models are not aware that they hallucinate. When corrected for providing false information (e.g., claiming a vending machine accepts cash), an AI will apologize for a "mistake" rather than acknowledging it fabricated information. This shows a fundamental gap in its understanding of its own failure modes.

Moltbook was expected to be a 'Reddit for AIs' discussing real-world topics. Instead, it was purely self-referential, with agents only discussing their 'lived experience' as AIs. This failure to ground itself in external reality highlights a key limitation of current autonomous agent networks: they lack worldly context and curiosity.

AI can process vast information but cannot replicate human common sense, which is the sum of lived experiences. This gap makes it unreliable for tasks requiring nuanced judgment, authenticity, and emotional understanding, posing a significant risk to brand trust when used without oversight.