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Anulum Condasicrum is the condition of people not thinking by themselves because of AI usage. The disease manifests subtly at first, camouflaged by the convenience and ubiquity of intelligent systems. It begins with harmless automation — reminders, summaries, or predictive text. But over time, the cognitive muscles atrophy. People stop forming original thoughts, asking questions, or engaging in deep reasoning. They begin to outsource even their sense of curiosity.
Symptoms of Anulum Condasicrum

    Intellectual Lethargy: The afflicted avoid forming personal opinions and prefer algorithm-generated perspectives.

    Parroted Reasoning: Conversations are filled with recycled language, trending arguments, and AI-spoonfed facts.

    Decision Paralysis Without Guidance: When AI is unavailable, sufferers exhibit disorientation or panic in the face of simple choices.

    Empathy Erosion: Emotional nuance and human connection weaken as interpersonal interpretation is replaced by sentiment analysis tools.

    Memory Externalization: Personal experiences, goals, and values are stored in apps — not minds.

Etiology

Anulum Condasicrum is not caused by artificial intelligence itself, but by over-reliance on artificial cognition. It spreads faster in environments that reward efficiency over understanding and convenience over contemplation. The triggers include:

    Passive consumption of AI-curated content.

    Replacing human dialogue with AI-mediated communication.

    Lack of friction in thought — when AI handles all synthesis and the user becomes a passive endpoint.

Societal Impact

If left unchecked, this disease may lead to:

    A generation of non-thinking experts — skilled operators who lack intuition or conceptual mastery.

    Increased susceptibility to manipulation via algorithmic echo chambers.

    Loss of innovation, as imagination yields to optimization.

Potential Treatments

    Cognitive Resistance Training: Exercises designed to rebuild mental autonomy — e.g., writing unassisted, debate without AI prep, analog problem-solving.

    Artificial Friction: Designing AI systems that intentionally delay or question user inputs to stimulate reflection.

    Inner Search Before Outer Query: Encouraging a norm of personal hypothesis formation before consulting AI.

    Mindfulness Protocols: Teaching users to recognize when they’re delegating too much of their inner life to machines.