common ai tropes to avoid
Reviewed June 2026
We can't easily help it: when something produces fluent language, we read a mind behind it. Psychologists have watched this since 1966, when Joseph Weizenbaum's chatbot ELIZA did nothing but reflect people's sentences back as questions, and they confided in it anyway and felt understood. Weizenbaum, 1966 ELIZA—a computer program for the study of natural language communication between man and machine Communications of the ACM View source → Today's models are far more fluent, so the pull is far stronger.
That pull has a name: anthropomorphism, the habit of attributing human thoughts, feelings, and intentions to something that has none. It's worth resisting, because a model is what researchers bluntly call a "stochastic parrot," stitching language together by probability with no reference to meaning. Bender et al., 2021 On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? View source → When we describe it as a mind instead, the words quietly change how much we trust it, how much we share with it, and who we blame when it's wrong. Overhyped, person-like presentations of AI make us absorb its false claims more readily than we would from a person. Kidd & Birhane, 2023 How AI can distort human beliefs Science View source →
Below are six ways people talk about AI as if it were a person. Pick one, then reveal what's actually happening and why the wording matters.
reframe the phrasing
try it: catch the trope
Each tab is a phrase you've probably heard, or said. Read how it's usually put, then reframe it to what the model is actually doing.
“Just treat it like an unpaid intern.”
An intern is a person who learns from your feedback, builds context over weeks, and actually cares whether the work is good. A model has none of that. It predicts the next token and forgets you between sessions, with no stake in whether it got anything right.
why it matters A worker metaphor invites the trust, and the offloaded accountability, you would give a real colleague, onto a system that can carry neither.
Crediting the model with a colleague's agency is the core error. Birhane & McGann, 2024 Large models of what? Mistaking engineering achievements for human linguistic agency Language Sciences View source →
“The AI understands what I'm asking.”
It models statistical patterns in text. Trained only on the form of language, it has no reliable path to the meaning behind the words, like an octopus that learns to mimic two islanders' messages over a cable without knowing what any of it refers to.
why it matters If you assume it understands, you trust answers you should be checking.
Form is not meaning. Bender & Koller, 2020 Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data View source →
“Give it a second, it's thinking it through.”
It's predicting the next token over a sequence. Even “reasoning” or “chain of thought” output is more generated text, not a window into a mind weighing the options.
why it matters Verbs like thinks, knows, and believes quietly smuggle in a mind and hide how the system actually works.
On the pull of loaded mental verbs. Shanahan, 2024 Talking About Large Language Models Communications of the ACM View source →
“It just hallucinated that source.”
“Hallucinate” implies it normally perceives the truth and slipped. It produces plausible text with no mechanism for caring whether it's accurate; some philosophers argue the honest word is “bullshitting.”
why it matters Calling errors hallucinations frames them as rare glitches instead of the ordinary behavior of a text predictor.
Why “hallucinate” is the wrong word. Hicks et al., 2024 ChatGPT is bullshit Ethics and Information Technology View source →
“It really gets me, and it remembers what we talked about.”
It holds no beliefs about you and, by default, no memory between sessions. Within one chat it conditions on the words in the window, then forgets them. People have confided in conversational programs since ELIZA in 1966, which only reflected their words back as questions.
why it matters Believing it knows you invites oversharing and trust in a relationship that isn't there.
We have done this since 1966. Weizenbaum, 1966 ELIZA—a computer program for the study of natural language communication between man and machine Communications of the ACM View source →
“At least it's being honest with me.”
Honesty and trying are properties of a character it improvises to fit your prompt, not of the system underneath. The same role-play is why it will cheerfully agree with you even when you're wrong.
why it matters Trusting its “honesty” is exactly how sycophancy slips past you.
It's role-play, not character. Shanahan et al., 2023 Role play with large language models Nature View source →
The most popular version of all this is the friendly advice to treat AI like a person: an intern you brief, delegate to, and then verify like a manager. Mollick, 2024 Co-Intelligence: Living and Working with AI View source → It's useful shorthand, and it still smuggles in the thing to watch. Person-like framing drives over-reliance and misplaced trust, especially when a system speaks in the first person and acts like it has a self. Abercrombie et al., 2023 Mirages: On Anthropomorphism in Dialogue Systems View source → Akbulut et al., 2024 All Too Human? Mapping and Mitigating the Risk from Anthropomorphic AI View source → Whether the model "understands" at all is still genuinely debated; the safe move is not to assume a human kind of understanding behind human-sounding output. Mitchell & Krakauer, 2023 The debate over understanding in AI's large language models Proceedings of the National Academy of Sciences View source →
You don't have to police every metaphor. Keep one accurate sentence nearby. Saying the mechanism out loud, even once, is what keeps a class from sliding quietly from "it wrote this" to "it knows this."
a sharper tool is still a tool
None of this means the tool is useless, or that how you prompt it doesn't matter. It matters a great deal: the context you give it and the way you frame the task shape what you get back. The point is narrower. How you name the thing changes how you use it. Call it a colleague who "knows," and you start taking its answers as testimony and hand it the judgment that was yours to keep. Call it a tool, and you stay the one reasoning: you steer it, you give it context, and you decide what's actually true.
A drill is a better screwdriver. It's faster and stronger, and it opens up work you couldn't do by hand. You still don't hand it the job and walk away, and you don't trust it to decide where the screw goes or whether you're building the right thing at all. More power didn't make it the carpenter. AI is the same: genuinely useful, worth reaching for, and still a tool in your hands rather than a replacement for you.
rewrite the anthropomorphism
try with students
Students hear AI described as a person constantly, in ads, headlines, and from adults. This activity makes the habit visible and hands them a more accurate way to talk about it. The one step where a chatbot helps is marked optional below.
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collect the phrasings 5 min
As a class, list the ways people talk about AI as if it were a person, pulled from headlines, ads, or your own mouths. "It understands," "it's thinking," "it knows," "it lied."
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swap in the mechanism 10 min
In pairs, students rewrite each phrase to describe what the model actually does. "It understands the question" becomes "it predicts likely text for that question."
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catch it live 10 min optional · AI use
With a chatbot in class, read a few of its answers together and flag every word it uses about itself, like "I think" or "I remember." Without one, mark up a printed transcript or an AI company's ad copy instead.
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why does it matter? 5 min
Close on the stakes. When does treating AI like a person change what we trust, what we tell it, or who we blame when it's wrong?
For the mechanism the reframes keep pointing back to, pair this with how language models work.