ELIZA Knew You Before ChatGPT Did
The first chatbot was already a mirror — and a secretary closed the door on its inventor
Previously: When the Embroidery Was Real but the Artist Wasn’t.
For years I carried a wrong memory of the first chatbot. I was certain its name was Lucy — something about the timeline placed it in the early 1970s, maybe a university lab, an experiment in conversation. I even told people this. Lucy, the first chatbot. Very confident, completely wrong.
The name was ELIZA. Developed between 1964 and 1967 at MIT by a computer scientist named Joseph Weizenbaum, it ran on an IBM 7094 mainframe as part of Project MAC. I got the decade wrong, the name wrong, and apparently held onto that false certainty for a long time. I’m sharing the error here not because memory mistakes are interesting in themselves, but because the error fits — ELIZA was specifically designed to make you feel like you understood what was happening when you didn’t. That the first chatbot would produce a misremembered echo of itself feels almost too appropriate.
The Doctor Will See You Now
Weizenbaum named ELIZA after Eliza Doolittle — the protagonist in Shaw’s Pygmalion, the working-class woman trained to speak with an upper-class accent until she sounds like something she isn’t. That framing was deliberate. ELIZA was a general-purpose engine that relied on external scripts to give it shape. It had no fixed knowledge of the world, no opinions of its own. Feed it a script and it became whatever the script described.
The most famous script was called DOCTOR, and it simulated a non-directive psychotherapist in the style of Carl Rogers. Rogerian therapy involves reflecting the patient’s own statements back to them — the therapist withholds diagnosis, withholds judgment, and mostly asks the patient to go further, to say more. “How does that make you feel?” “Tell me more about that.” “Can you elaborate?” This style was a pragmatic technical choice, not a philosophical one. A non-directive therapist isn’t expected to know things. It doesn’t need a database of facts or medical knowledge. The patient does all the semantic work. The mirror just needs to hold still.
What ELIZA actually did under the hood was pattern-matching. It scanned your input for keywords — words like “mother” or “dream” carried high priority, words like “was” carried low. Once it identified the highest-priority keyword in your sentence, it broke your input apart using template rules and reassembled it into a question. If you typed “I am worried about my mother,” ELIZA matched the word “mother,” isolated the surrounding text, swapped the pronouns, and reflected it back: “Why are you worried about your mother?” The process extracted nothing. Understanding wasn’t involved, and meaning wasn’t the point — it was pure syntax, operating on pattern and substitution, looping on a turn counter that made it seem like it remembered things it had simply queued.
The question mark character couldn’t even be used in the conversation. The CTSS operating system interpreted it as a line-delete command. ELIZA was working around hardware constraints at the same time it was simulating empathy — which, for an IBM 7094 in 1966, was apparently a full workload.
The Secretary
Weizenbaum had a secretary at MIT. She had watched him build ELIZA from the beginning. She had seen him write the code. She understood, at whatever level an informed observer in the mid-1960s would understand, that this was a deterministic text-processing script running on shared computing time — not a mind, not a counselor, not a presence.
