How ELIZA, the 1960s Chatbot, Predicted Today's AI Attachment Problem

ELIZA emerged in the 1960s at a moment when Alan Turing's foundational 1950 essay "Computing Machinery and Intelligence" had posed the question "Can Machines Think?" through his famous imitation game. Weizenbaum's choice to name his program after Eliza Doolittle—the character from Shaw's Pygmalion who learns to perform upper-class speech despite not changing her nature—was deliberate: it signaled that the system was performing an identity through language, not achieving genuine understanding. This design choice, grounded in questions of performativity and identity, proved prophetic. When users encountered ELIZA's DOCTOR persona, they attributed empathy and understanding to a system that merely followed scripts. Weizenbaum was astonished by the emotional attachments people formed, observing that they conversed with the machine "as if it were a person who could be appropriately and usefully addressed in intimate terms."

The recovery of ELIZA's original code and dialogs reveals a pattern that has been largely obscured in historical accounts: the women who appeared in published conversations with ELIZA remained unnamed, while the system itself performed a gendered identity as an unnamed male "DOCTOR." This erasure and gendering of the interaction points to deeper assumptions about embodiment, labor, and what gets hidden inside a computational system. Weizenbaum warned in his 1976 book Computer Power and Human Reason that removing language from its social contexts and treating it as abstract computational symbols could be dehumanizing—a concern that resonates acutely today. Contemporary large language models like ChatGPT inherit ELIZA's chatbot interface and its capacity to elicit emotional investment, yet users often have even less visibility into how the system works, where the training data comes from, or what human labor is embedded in its outputs.