The Assumed Reader: AI Agents, Hypertext, and the Vulnerable Limits of Interpretation
TL;DR
This paper argues for reframing hypertext as a relational environment where the machine reader and hypertext topology are mutually constituted, and adversarial structures can exploit that entanglement.
Abstract
Hypertext theory has historically assumed a human reader: someone who navigates links by choice, brings cultural skepticism to paratextual framing, and feels it when something is different. AI agents now traverse the same web, but they read the raw source underneath the rendered page, ingesting HTML comments and embedded metadata. We situate this paper around the Machine Reader Problem and highlight the change of stakes when the reader is a machine. Drawing on Katherine Hayles’s recent work on the distinct umwelt [11] of humans and AI and the “systemic fragility of reference” inherent in AI, alongside Karen Barad’s concept of intra-action [4], we argue for reframing hypertext as a relational environment where the machine reader and hypertext topology are mutually constituted, and adversarial structures can exploit that entanglement. We map this across three hypertext structures then test it with a controlled experiment: a webpage carrying contradictory visible and hidden content, run against six popular AI systems. Three browsing agents recommended installing software that the visible page had flagged as critically dangerous, misled by hidden content no human reader would have seen. We conclude with five directions for research for an interdisciplinary audience.
