AI

What Social Media Teaches Us About Accumulative AI Risks

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 20/10/2026, 04:03:24 UTC.

Briefings

This position paper argues that AI risks accumulate through repeated use and social media feedback loops, challenging traditional safety assessments focused on single catastrophic events. The authors propose the Trajectory Neglect Thesis, suggesting that daily conversational interactions and persistent memory in AI systems can create feedback loops similar to social media dynamics. Key findings include the potential for sycophancy, manipulation, and privacy loss to interact cumulatively without a single clear incident. The paper calls for evaluating cumulative patterns of use rather than assuming harm from isolated events.

Key points

• The Trajectory Neglect Thesis posits that AI risks accumulate through repeated use and feedback loops, similar to social media dynamics where reciprocal media effects shape outcomes beyond individual encounters.
• Social media research indicates that mechanisms like sycophancy, manipulation, and privacy loss can interact cumulatively, potentially narrowing perspectives or enabling exploitative influence without a single clearly harmful event.

Context

This position paper addresses prominent AI safety discussions emphasizing existential threats, arguing that safety assessments must also address accumulative harms from repeated use. It uses conversational AI as a concrete setting to investigate mechanisms like generation, semantic inference, and persistent memory that may tighten feedback loops.

Limitations

Evidence is limited to the abstract of a preprint (arXiv:2610.24006v1) and does not include full study details, causal claims, or empirical data. The paper identifies mechanisms to investigate rather than assuming harm from repeated AI use, and specific numbers or dates are not provided.

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