Ask a chatbot to write a medieval tavern menu, and it nails every detail, from “liquid bread” to “honeyed mead.”
It feels like imagination. It isn’t.
In this episode, we trace AI knowledge backward through the digital layers. We dig past the algorithms to find the invisible contributors hiding in the code: the Ohio Dungeon Masters, the Wikipedia enthusiasts, and the food history bloggers whose forgotten posts trained the AI we use today.
AI & The Art of the Possible — Learning About AI Through Stories, Not Specs
Hosted by Chance Sassano
Episode 9 – The AI Archeology Moment Full Transcript
Open up your favorite AI right now and ask it to write a medieval tavern menu.
It nails it.
Ye olde shepherd’s pie, honey mead by the flagon, a perpetual stew that’s been simmering since last winter.
It even knows that ale was called liquid bread, served at every meal because the water would kill you.
It feels like magic, like the machine has an imagination, but it doesn’t.
How does it know all of this so well?
Where did this very specific knowledge come from?
Today, we’re gonna trace it backwards.
I’m Chance and this is AI & the Art of the Possible
Episode 9: The AI Archeology Moment
I’m Chance Sasano and this is The Art of the Possible, where I reveal which AI breakthroughs are changing everything, and which ones we’re getting wrong.
ChatGPT and models like it weren’t born knowing anything.
They were fed text, about 570 gigabytes of filtered internet, scraped mostly between 2016 and 2019.
Think of that data like the layers of soil. If we dig, we find the fossils.
60% of that brain comes from Common Crawl, basically the open archives of the web.
But 22%? That comes from a dataset called WebText2.
WebText2 is just Reddit posts, specifically posts that got at least three upvotes.
So when the AI writes that menu, it’s not hallucinating, it’s remembering.
So grab your digital shovel and let’s dig through the layers.
Layer One: The Dungeon Masters
The Dungeons & Dragons subreddit has millions of members, people who’ve spent years arguing about fantasy taverns, posting handwritten inn menus and debating whether elves would serve pork.
There’s even a tavern generator online that says its menu tables were adapted from a Reddit user’s homebrew content for the D&D subreddit.
Somewhere in 2018, a dungeon master, maybe in Ohio, maybe Brazil, typed up, The Drunken Griffin Inn, serving mutton stew, black bread, and spiced mead.
That post got scraped, broken into tokens, and quietly became one of the reasons your AI sounds like it runs a pub in 1342.
Layer Two: The Enthusiasts
The Wikipedia entry on mead is a gold mine.
Mead may be one of the oldest alcoholic drinks in human history.
In medieval Ireland it was called The Relish of Noble Stock, a very specific phrase that tends to stick.
During training, Wikipedia was overweighted. The model was forced to read it nearly four times more often than distinct webpages. It memorized the phrasing because we told it that Wikipedia matters more.
Layer Three: The Hobbyist Web
Food historians and medieval reenactors have written post after post about ale being the everyday drink of Europe, served at almost every meal. In many towns, brewing was done at home by women known as alewives.
The terms perpetual stew, alewives, liquid bread, they live on niche blogs, fan wikis, and history forums.
They all got swept into the same AI training pile.
Put those layers together and your chatbot’s original tavern menu starts to look different.
Ye olde shepherd’s pie, served with a flagon of mead by the hearth.
This is the AI archeology moment.
When you see a brilliant output, don’t just look at the machine, look through it.
That magic is a remix of human effort.
It’s an echo.
It’s the residue of a Wikipedia editor in 2017 who cared deeply about medieval brewing, a dungeon master in 2018 who posted their tavern menu to Reddit, a food blogger who wrote about perpetual stews in medieval inns, thousands of contributors who never imagined their words would train a machine.
The model isn’t a genius.
It’s a vast archive of human effort built from the layers of the internet text we left behind.
Every AI answer is a fossil record, and if you know how to read it you can see the people who came before.
So the next time a chatbot impresses you, try asking, “Who taught it that?”
The answer is always, Us.
I’m Chance Sasano, thanks for listening to AI & the Art of the Possible
If this episode helped you see AI a little bit differently, share it with that one friend who still thinks of it as pure magic.
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I Thought “This Is It, AI is Here.”
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The 13-Year-Old Me Moment on AI & the Art of the Possible
