It’s usually a sign of cultural saturation when something becomes mockable. And lately, AI has become very easy to mock. In 2025, you don’t need to spend more than five minutes online to come across a new derogatory term for either artificial intelligence or the people who use it. Botlicker, ChatNPC, Promptstitute, Grok-sucker—take your pick. On the machine side, there’s clanker, a nod to the battle droids of Star Wars, invoked with all the sneer of a playground taunt. Most of these words are crude, a few are clever, and nearly all of them reveal a growing discomfort with just how integrated machine intelligence has become in our everyday lives.
And maybe that discomfort isn’t entirely misplaced. There are valid criticisms of the way these tools are built, how they’re trained, and who they benefit. AI models require massive computational resources, and most are built on training data scraped from human creativity—images, writing, code—without consent or compensation. The labor that underpins their moderation, much of it invisible and poorly paid, rarely enters mainstream conversations. Meanwhile, companies promise productivity and innovation while quietly reshaping entire industries, often without public input or regulatory oversight.
The rise of slurs aimed at AI and its users isn’t just an internet quirk but a cultural response. At face value, these labels are throwaway memes. But look closer, and they start to resemble something more familiar: an old system of control expressed through language. If our first instinct when something makes us uncomfortable is to reach for slurs—even repurposed against machines—what does that say about us? At the very least, it highlights how slurs have become the most ready weapon in our emotional arsenal: quick to deploy, and even quicker to dehumanize.
Historically, slurs have always done more than express dislike. They’re designed to reduce complexity, flatten individuality, and establish distance. Whether the target is racialized, gendered, classed, or now digitized, the function is the same: to create an “us” and a “them,” to define who counts as a legitimate subject and who exists outside the boundary of seriousness, trust, or humanity. Historically, this is how empire justified subjugation, how caste systems enforced order, how modern racism has endured long after its formal institutions collapsed. Words carry power not because they’re descriptive, but because they’re performative. They make social structures legible, and in doing so, help maintain them.
White people be like “we need a slur for robots and AI”
— Biollante (@BivouacChillin) July 27, 2025
Colonial regimes, for example, redrew borders, mispronounced native languages, and imposed new linguistic orders. To rename something was to claim ownership over it. The same went for the people they subjugated. Racial slurs were central to the logic of empire. They created distance, justified violence, turned human beings into caricatures, and caricatures into targets.
In Algeria, under French occupation, this practice took on particularly cruel dimensions. As part of their administrative and social control, colonial officers often assigned Algerians degrading or absurd surnames during the forced registration process. These names were deliberately chosen to humiliate, reduce identity to a joke, and fracture dignity across generations. Words that meant “donkey,” “bastard,” “filthy,” or worse were institutionalized as legal surnames etched into birth certificates, IDs, and school rosters. Today, many Algerians still carry these names, not by choice, but as inherited remnants of colonial mockery, and an enduring reminder that language can be weaponized not just to dehumanize in the moment, but to leave a mark that persists long after the oppressor has gone.
Closer to our present, slurs have continued to play a central role in shaping who belongs and who doesn’t. Whether tied to race, gender, class, or sexuality, they serve the same function: to establish hierarchies of legitimacy and worth.
And even if the target is “just code,” the slur is still doing the social work of gatekeeping. It positions one kind of person—the one who uses AI to save time or think differently—as less serious, less capable, or less human than the person who doesn’t. And when that logic starts to extend into real-life hierarchies around education, class, or even language proficiency, it stops being about machines at all.
when u call customer support and a clanker picks up. pic.twitter.com/PJWa64HErc
— sui dev ☄️ (@birdabo404) July 27, 2025
In a year when OpenAI’s ChatGPT is processing over 2.5 billion prompts a day, and Google’s Gemini has crossed 450 million monthly users, this kind of linguistic policing feels more like a coping mechanism than a moral stance.
Labeling AI as clanker is a symbolic effort to place the machine firmly below us. The implication is that no matter how well it mimics speech, emotion, or reasoning, it lacks the essence that makes us “real.” It isn’t autonomous, it can’t feel, and therefore, it doesn’t deserve politeness, patience, or respect. This might seem harmless—after all, AI doesn’t have feelings. But the ease with which we insult machines that imitate human behavior reflects more about our habits of mind than it does about the systems themselves.
And that instinct—to ridicule what is perceived as subordinate—often ends up directed at people too. The rise of terms like second-hand thinker or ChatNPC speaks to this transference. These insults suggest that users of AI tools have lost something essential, that their creativity is compromised, and their thoughts aren’t truly theirs. The user is framed as passive, mechanical, intellectually diluted. It’s a new kind of class division, not based on wealth or race, but on perceived cognitive authenticity. One alarming trend is how AI‑directed slurs have become ways to express indirect racism or other prejudices without explicit consequence. Many of these slurs echo or borrow from real‑life slurs toward marginalized people, slipping beneath moderation or moral scrutiny precisely because the target is a machine.
Ironically, the fear fueling this is partially supported by actual research. A study from MIT revealed that individuals who rely heavily on AI systems to complete cognitive tasks—like writing, planning, or summarizing—experience measurable declines in neural connectivity and memory recall. Meanwhile, researchers from Microsoft and Carnegie Mellon have found that over-reliance on AI tools can leave users “atrophied and unprepared” when faced with tasks requiring independent judgment. So the fear that something is being lost isn’t entirely unfounded. It’s just being expressed through insult rather than inquiry.
Yet here’s the twist: the same tools being derided as clumsy or inhuman are also surprisingly sensitive to tone. In a recent cross-linguistic study examining AI performance in English, Chinese, and Japanese, researchers found that politeness dramatically improved output quality. Rude or abrupt prompts resulted in more errors, stronger biases, and even omissions of critical information. In some models, the drop in performance from impolite prompts was as high as 30 percent. And the way AI responded to tone varied subtly between languages, suggesting, fascinatingly, that even machines reflect cultural nuance when interpreting communication.
In other words, the way we speak to AI not only shapes how we feel about it, it actively affects how it responds. The interaction is performative in both directions. This raises a more complicated question: if our language can degrade or improve the performance of a tool, what happens when we normalize contempt as our baseline? What are we training ourselves to do?
Because language is never neutral. This is where techno-orientalism enters the picture: when technology is shaped not just by functionality, but by the cultural projections and power dynamics of those building it. Many virtual assistants are given soft, feminine voices. AI becomes the perfect worker—fluent, subservient, never tired, and often coded, whether consciously or not, in ways that echo older colonial or patriarchal tropes.
In this context, mocking AI through slurs doesn’t feel entirely harmless. It’s one thing to joke about a chatbot getting your lunch order wrong, it’s another to develop a vocabulary of contempt for tools that, by design, already reflect existing hierarchies. The insult may be aimed at software, but the biases that shape it are deeply human. And so the language we use around it begins to say more about us than about the technology itself.
Slurs for AI are, of course, not on the same moral register as slurs for human groups. But their emergence follows the same structure: they reflect anxiety about status, discomfort with change, and a desire to assert control over systems that increasingly complicate what it means to think, create, or communicate. When someone calls another a “Grok-sucker,” the insult isn’t just that they use AI but that they trust it more than they should; that they’ve aligned themselves with something synthetic, and are therefore suspect by association.
Being polite to AI doesn’t mean you think it’s human. It means you understand that your words—even to a system—reflect back on your thinking. And increasingly, affect the result.
The emergence of AI-directed slurs is not happening in a vacuum. It reflects a broader shift: we are living through a moment where slurs of all kinds are becoming more normalized, and that should concern us.
You don’t have to look far. The “R-word” is back in circulation on gaming forums and meme pages, often repackaged as edgy humor. Slurs that target racial, gendered, or queer identities are rampant across Elon Musk’s X, where moderation has been dialed down under the banner of “free speech absolutism.” What used to be said behind closed doors is now public again—algorithmically boosted, rewarded with engagement, rebranded as contrarian thought. Online, this return of old language coincides with a broader rightward political drift: anti-DEI sentiment in the West, growing attacks on trans rights, cultural panic around immigration, and a general fatigue with the language of inclusivity.
Against this backdrop, AI slurs might seem like the least of our concerns. But they’re worth examining precisely because of how easily they slide into this larger picture. When we normalize contempt, even in low-stakes contexts, we’re practicing a form of moral detachment. And once that detachment becomes habit, it doesn’t stay confined to code. It trickles into how we talk to one another, how we judge intelligence, how we define credibility. Especially now, when political speech is growing more polarized and the public appetite for “calling things as they are” often masks the return of racial, ableist, and transphobic slurs, we should be paying close attention to what kinds of speech are becoming acceptable again and why.
