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In July 2020, 4chan’s video-game discussion board looked much like the rest of the notorious online forum. There were elaborate, libidinal fantasies involving “whores” and “dragon cum,” and comments on how long a gamer had to wait “before my dick can get up for another beating,” as one put it.

And yet, as the gamers discussed such things, they were also making a discovery of significance to the AI industry. Some of them were playing AI Dungeon , a new text-based role-playing game that was essentially an AI version of Dungeons & Dragons . In endlessly generated fantasy-world scenarios, players described actions like “pick up the sword” or “tell the troll to go away,” and the computer responded with the action that followed.

In addition to asking the game’s characters to engage in various sex acts (naturally), the 4chan gamers also asked them to do math problems. That sounds strange, of course, but AI Dungeon was powered by OpenAI’s GPT-3, and the gamers knew that they were among the first people to probe the capabilities of this new large language model. This was more than two years before the release of ChatGPT, and the model was famously bad at math. It frequently failed at simple arithmetic. But when they asked a character in the game to do a math problem and provide a step-by-step explanation, one of them wrote, the LLM was “not only solving math problems but actually solves them in a way that fits the personality of the fucking character.”

The players had come upon a new feature—what’s known in AI today as “chain of thought.” Essentially, it means that the model explains the steps required to solve a problem, in addition to giving an answer. Asking the model for a chain of thought also seems to improve the accuracy of its answers to certain kinds of problems. The gamers on 4chan recognized the significance immediately, and posted examples on Twitter.

Recently, the tech industry has promoted chain of thought as a revolution in technology, and a reason to get excited about AI all over again. Researchers at Google claimed in a paper to be “the first” to elicit a “chain of thought” from a general-purpose LLM, more than a year after the 4chan gamers shared their findings. (This claim was removed from subsequent versions of the paper, which still did not acknowledge the gamers, though at least one other research paper has.) And in the past couple of years, companies have begun to claim that their chatbots are not just getting math problems right; they are actually thinking about them. OpenAI wrote in 2024 that its “o1” model “thinks before it answers,” and Google claimed that Gemini 2.0 Flash Thinking Experimental was “capable of showing its thoughts.” Companies started referring to their models as “ reasoning models, ” ostensibly a new kind of product from an LLM.

Amid all this hype, the 4chan history is instructive. 4chan gamers, for all their brash language, have tended to speak in more levelheaded—and accurate—terms than the AI industry about how the models work. Last year, for example, Anthropic published a long and serious-looking article , “On the Biology of a Large Language Model.” Its visual presentation mimicked scientific publications, with sophisticated-looking diagrams and equations. But on every topic, the article described the operation of the LLM in terms of a human mind. It said the LLM “plans” its writing in advance, “generalizes” its knowledge, and can be “unfaithful” to its chain of thought (meaning, the article explains, the LLM is occasionally “bullshitting”).