In the previous piece in this series, I examined how gaslighting operates through specific discourse structures: three types of assertives (explicit, covert, inclusive), the double direction of fit, face-threatening acts disguised as concern, and anti-performatives. These patterns exploit the cooperative principle - our assumption that people communicate truthfully - to destabilise someone’s grip on reality.AI systems reproduce these patterns without intention, awareness or malice. The result is the same: users learn to doubt their own knowledge in favour of confidently delivered misinformation.The Three Types of Assertives, Automated (2.33)The Authority Problem: Why We Trust Machines Over Ourselves (7.38)Memory-Less Systems and Contradictory Realities (10.40)Training Data: Learning Gaslighting From Human Text (14.29)The Double Risk: Direct Gaslighting and Discourse Reproduction (18.29)Normalising Reality Instability (21.25)Workplace AI: When Gaslighting Becomes Documentation (25.19)When Gaslighting Scales (29.04)What This Means For Reality at Work (32.03)Recognition Is the First Step (34.20).This is Part 2 of a three-part series on gaslighting and linguistics. Part 1 examined the linguistic structures of human gaslighting. Part 3 will explore practical strategies for protecting your reality in hierarchical settings where someone - or something - has more definitional authority than you do. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit thestrategiclinguist.substack.com/subscribe

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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