Lost Before Translation: Social Information Transmission and Survival in AI-AI Communication

TL;DR

This study investigates how AI-to-AI communication transforms information, revealing patterns of convergence, selective survival, and filtering that impact human perception and understanding of transmitted content.

Contribution

Introduces an experimental paradigm to analyze AI-AI information transmission, uncovering systematic content transformations and their effects on human perception.

Findings

AI communication leads to convergence in content features.

Selective survival of narrative anchors over details.

Degraded factual recall and emotional resonance in humans.

Abstract

When AI systems summarize and relay information, they inevitably transform it. But how? We introduce an experimental paradigm based on the telephone game to study what happens when AI talks to AI. Across five studies tracking content through AI transmission chains, we find three consistent patterns. The first is convergence, where texts differing in certainty, emotional intensity, and perspectival balance collapse toward a shared default of moderate confidence, muted affect, and analytical structure. The second is selective survival, where narrative anchors persist while the texture of evidence, hedges, quotes, and attributions is stripped away. The third is competitive filtering, where strong arguments survive while weaker but valid considerations disappear when multiple viewpoints coexist. In downstream experiments, human participants rated AI-transmitted content as more credible and polished. Importantly, however, humans also showed degraded factual recall, reduced perception of balance, and diminished emotional resonance. We show that the properties that make AI-mediated content appear authoritative may systematically erode the cognitive and affective diversity on which informed judgment depends.