Why Is Everyone in Silicon Valley Talking Like That?

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.

Where I live, in San Francisco, 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.” [ Read: Machine Unlearning ] 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 historically evolved alongside technology.