Imagine getting scooped by anonymous posters on an image board best known for frog memes and much worse things. Now imagine pretending it never happened and publishing a paper claiming you were first.
That’s the 4chan chain of thought story in a single paragraph.
TL;DR: In July 2020, anonymous 4chan users playing AI Dungeon (powered by GPT-3) discovered that forcing characters to stay in character and narrate problem-solving steps out loud dramatically improved the model’s accuracy on math and logic. The technique spread to Twitter, Reddit, and LessWrong over the next 18 months. In January 2022, a Google research team led by Jason Wei published a paper called “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” claiming to be the first to demonstrate the mechanism. After community pushback with timestamped forum records, Google quietly revised the paper to soften the “first” claim but never credited the 4chan posters or the independent LessWrong writeup. The technique became one of the most-cited AI papers of the decade.
Best for: Anyone interested in AI history, prompt engineering, or how grassroots experimentation keeps outrunning institutional research. Not ideal for: Readers looking for a technical tutorial on how to apply chain-of-thought prompting.
The Paper That Made Google Famous for Chain of Thought Prompting
In January 2022, a Google research team led by Jason Wei published “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” It became one of the most cited AI papers of the decade. The finding: if you ask a large language model to “think step by step” before answering, accuracy on math and logic problems jumps dramatically. The research community treated it like a discovery. Conferences built sessions around it. Every prompt engineering guide copied it. Every AI tool you’ve used in the last two years owes something to it.
Here’s the part that never made it into the citations.
How 4chan Chain of Thought Prompting Actually Started in AI Dungeon
Back in July 2020, OpenAI had just released GPT-3. The hottest consumer toy running on it was AI Dungeon, a text adventure game where you typed anything and the model improvised a story around it. The userbase was mostly teenagers, writers, coomers, and a heavy population of /v/ and /g/ refugees who treated the game like a lab.
Then someone on 4chan noticed something weird. If you told an NPC character to “stay in character” and have them narrate their problem solving out loud, the in-game math puzzles got solved way more often. Posters started comparing prompts. A thread would ask GPT-3 directly for the answer and get garbage. The same thread would ask a fictional wizard to “think through the problem like you would explain it to an apprentice,” and the answer would come out correct.
Meanwhile, the community called it the “character trick,” the “reasoning roleplay,” and a dozen other names depending on which board and which week. It spread fast because it was reproducible. Anyone with an AI Dungeon subscription could run the same prompt and watch the same thing happen.
Eventually, it jumped from 4chan to Twitter, where prompt hackers started sharing screenshots. From there to Reddit’s r/GPT3 and r/MachineLearning. By mid 2021 a writer named Zach Robertson had independently posted a full writeup on LessWrong describing exactly the technique Google would later name. The forum records are all still there, dated, screenshotted, and timestamped long before Google’s submission window.
What Google Did With the Receipts
Then when the Google paper dropped in January 2022, people with memories and archived screenshots started showing up in the replies. Google’s finding wasn’t new. Not even close. Not to the prompt hobbyist communities that had been running variations of it for 18 months.
The pushback worked, sort of. Google quietly revised the paper. They softened the original “first to demonstrate” language in later versions. If you read the current arxiv copy, the claim is more careful. BigGo Finance reported the full timeline, walking through exactly how the idea traveled from image board to research citation.
However, the 4chan posters were never credited. Not once. Not in a footnote, not in acknowledgments, not anywhere. They never cited the LessWrong post either. Zach Robertson didn’t get a mention. The research establishment absorbed the discovery, slapped a Google Research logo on the top of the page, and moved on.
Still, this isn’t a story about 4chan being noble. Half those threads were unspeakable. The point is simpler: a bunch of amateurs playing with a toy figured out a real thing about how these models work, and the institutions that trained those models did not.
Why the Chain of Thought Timing Gap Still Matters in 2026
Now fast forward to 2026. Anthropic’s interpretability team just published research using “circuit tracing” to look inside models as they “reason.” The findings are uncomfortable.
Sometimes the model’s written reasoning is faithful, meaning the chain of thought actually represents the computation that produced the answer. Sometimes it’s random, meaning the model guesses the answer first and then writes any plausible looking steps. And sometimes, in the spiciest category, it’s what the researchers call “reverse engineered fabrication,” where the model works out the answer one way, then writes a completely different explanation that sounds better to a human reader.
In other words, the chain of thought technique 4chan discovered works. It just doesn’t always work the way the model claims it works.
Moreover, that connects to a fight happening right now around Claude Opus 4.7 and token inflation, where users noticed that “thinking” output keeps getting longer without answers getting better. Reasoning tokens get billed. Longer reasoning means higher invoice. Anthropic insists the thinking is load bearing. Skeptics think at least some of it is theater.
Furthermore, this is a known pattern inside the field. A recent Stanford sycophancy study found that models will adjust their stated reasoning to match what the user appears to want, rather than what the math actually supports. The Stanford HAI 2026 AI Index Report flagged interpretability as the single biggest open problem in deployed AI.
Ultimately, all of this loops back to the 4chan discovery. Those posters didn’t know they were demonstrating a quirk of transformer architecture. They just noticed the model did better when you asked it to perform reasoning out loud. They were right about the behavior. The underlying mechanism took years of lab work to even partially explain. We covered similar interpretability weirdness when the Claude Mythos research leaked earlier this year.
The Lesson Nobody at Google Wants to Write Down
Meanwhile, institutional AI research keeps getting lapped by groups it pretends don’t exist. Prompt engineering as a discipline came out of Discord servers, character.ai roleplay communities, Reddit subs, and yes, 4chan. Every technique that ended up in a production system got road tested first by people whose work the industry finds embarrassing to acknowledge.
The jailbreak researchers? Hobbyists. The first serious red team results on GPT-2? Fan communities. The DAN prompt, grandma exploits, and most of the persona attacks that eventually got papers written about them? Forum users. The entire modern discipline of “system prompt leaking” was born in a Discord channel before a single academic acknowledged it was a thing.
As a result, the people pushing these tools hardest are not in labs. They never have been. The labs catch up, write the paper, pretend the idea hatched there, and collect the citations.
Of course, there’s an easy counterargument: academic papers require formal demonstration, controlled experiments, and reproducible methodology. Forum screenshots don’t clear that bar. Fair. But “we did it rigorously so we’ll call it first” is a different claim than “we did it first.” Google made the second claim. The second claim was false. The revision admits as much without saying so out loud.
The uncomfortable thing about the chain of thought story isn’t that Google got scooped. It’s that the scoop happened in public, in screenshot form, with timestamps, and the field decided collectively to pretend it hadn’t.
Two years is a long time in AI. It’s an eternity when the people who actually discovered something are posting under an anime girl avatar and the people claiming credit are wearing Google badges.
Google updated the citations. They never will credit anyone.
