Silicon Valley has long had its own "dialect". Venture capitalists are always talking about the value of being "high agency" and making "orthogonal bets." But lately, tech vernacular has taken a peculiar turn — people have started describing themselves as if they were chatbots. If you misspeak, maybe you're "hallucinating." Don't know the answer to a question? That's because it's not in your "training data." Feeling forgetful? Perhaps you have a case of "context rot," a phrase that refers to the degradation of a bot's responses over a long conversation. "I've been telling my wife I have context rot for months," Conor Bronsdon, the host of an AI-focused podcast, told me.
Among people who work in tech, such comparisons are inescapable. "I've described myself as high temperature," a friend recently told me; in AI-speak, this means he is prone to randomness. AI experts now talk about updating their "weights" when they learn something new and training on "synthetic data" when going over internal thoughts. "Humans have a huge base model trained over billions of years of evolution," one software engineer wrote on a popular tech forum. "It's impressive how quickly we learn, but it's arguably comparable to fine tuning."
Such speech easily comes off as unsettling, if not aggressively bleak: Why describe beautiful, tender life in detached, algorithmic terms? At the same time, language is a fossil record of previous technological revolutions, and if this current transformation is anything like previous ones, some of this new slang may well stick around. AI experts have repeatedly cautioned that anthropomorphism, or the tendency to attribute human qualities to nonhuman objects such as chatbots, can be misleading. But now the reverse is occurring in everyday speech — a sort of modelmorphism, wherein people describe themselves as if they were large language models.
The comparisons really took off a few years ago, after a group of AI researchers argued that language models are "stochastic parrots" that link together language based on statistical patterns without possessing any understanding of meaning. Dissenters retorted that humans are also "stochastic parrots." As one software engineer riffed, "Humans are basically a sophisticated Markov chain. They are very good at pattern matching, but have no understanding of anything."
Language has always evolved alongside technology. Expressions such as running out of steam and cog in the machine entered common speech after the Industrial Revolution, and nobody takes them literally: when someone says, "My gears are turning," there are no gears actually moving in their brain. The same goes for "context rot." Reaching for the newest machine to explain the mind is an old habit. Long before chatbots, philosophers compared the brain to a central telephone exchange.
For now, these analogies are being made mostly by those working in the AI industry. But over time, some of the metaphors might start to seep into everyday language. Some of the AI slang isn't introducing new phrases into English so much as transforming the meaning of existing speech. Take the word hallucinating, which is used to describe AI models confabulating. People are "taking a term created for what the human mind is capable of doing, projecting it onto AI, and then projecting it back onto humans with an AI flavoring," Naomi Baron, a linguist and professor emerita at American University, explained.
None of this is to say every AI-inflected term should get a free pass. I've started chastising my friends when I catch them unironically referring to themselves as chatbots. Using LLM metaphors to describe our minds could lead to a reductive and dehumanizing understanding of human cognition.
Still, the people in tech I spoke with told me that using LLM-speak to describe their own mind helps them reflect on their cognitive habits. Perhaps my great-grandchildren will grow up saying things such as "I have context rot" and "That's not in my training data" without pausing to remember that those phrases first applied to machines.