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    Home » Researchers Coin a Term for AI Job-Replacement Anxiety
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    Researchers Coin a Term for AI Job-Replacement Anxiety

    Tech DrogoBy Tech DrogoFebruary 18, 2026Updated:February 19, 2026No Comments2 Mins Read
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    Workers haven’t seen clear, large-scale AI-driven job losses yet, but the fear of replacement is already hitting hard. Researchers now argue that constant “AI will take your job” messaging can trigger real psychological distress, even before layoffs happen.

    They propose a new term for this pattern: Artificial Intelligence Replacement Dysfunction (AIRD). The paper describes AIRD as job-displacement anxiety tied to AI that can show up as stress, insomnia, depression, and identity confusion, along with deeper worries about relevance, purpose, and employability.

    Why “AI job anxiety” is spiking even without mass layoffs

    Public worry has risen fast. A Reuters/Ipsos poll found 71% of Americans were concerned AI could permanently put “too many people out of work.”
    A Pew Research Center survey found 52% of U.S. workers feel worried about AI’s future workplace impact.

    This gap—big headlines, uncertain reality—creates a constant threat signal. It also lands hardest on early-career roles and jobs that feel easiest to automate, where uncertainty can turn into chronic stress.

    What the AIRD framework suggests for support and recovery

    The researchers outline a clinical-style framework that can help providers screen for AIRD-like symptoms and separate them from other causes. They also point to Cognitive Behavioral Therapy (CBT) and related cognitive restructuring techniques to build resilience and restore a stable sense of self.
    If fear of AI replacement is affecting sleep, mood, or daily functioning, a licensed mental health professional can help.

    Eco-friendly SEO angle: sustainable AI adoption reduces human and digital waste

    Responsible AI rollouts can be greener and healthier at the same time. When companies invest in reskilling, set clear job transition plans, and use efficient models instead of brute-force automation, they reduce burnout-driven turnover and cut “redo cycles” that waste compute and energy. This supports workplace wellbeing, lowers operational waste, and encourages sustainable AI adoption over hype-fueled disruption.

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