How a Clever Guessing Game Became the Brains Behind AI
Imagine you’re playing a word-guessing game with a friend.
Before we talk about how AI affects our planet’s energy and our own emotional energy, we will spend a moment understanding the basics. When we know how these systems actually work, we gain clarity and strength. We become more intentional, more creative and more confident in how we use technology.
This article is our starting point.
Imagine you’re playing a word-guessing game with a friend. You say the first word: “Once…”
Your friend guesses the next word: “upon.”
Then you say, “a…”
They say, “time.”
It feels almost magical, right? But underneath the magic is something simple: your friend is predicting what usually comes next.
Believe it or not, that small, almost boring idea is the ancestor of today’s giant AI systems systems that can write essays, answer questions, and even help you brainstorm your science project. The family tree begins with something called a Markov chain, and grows into something called a large language model, or LLM.
Let’s walk through how the guessing game grew up.
Where It All Started: The Markov Chain
A Markov chain is basically a rule:
“Choose the next word based only on the word right before it.”
That’s it.
It doesn’t remember whole sentences. It doesn’t understand meaning. It’s like a goldfish with math powers.
If you fed a Markov chain a million fairy tales, it might learn that after the word “Once”, the most common next word is “upon.”
After “upon”, the most common next word is “a.”
After “a”, you get “time.”
So it can write things like:
“Once upon a time a dragon lived in a…”
It’s not thinking. It’s just following probability trails.
Still, for its time, it was a big deal. Computers could predict text.
But it had a problem.
The Big Limitation (and Why It Matters)
A Markov chain doesn’t remember what happened five or ten words ago.
It lives moment to moment.
So if you were writing a story about a dragon who meets a robot who meets a princess, the Markov chain would forget the dragon by the next paragraph. It can’t keep track of big ideas, themes, or even who a sentence is about.
It can predict the next word.
But not the meaning of what it’s writing.
That’s where people started asking a bigger question:
What if a computer could look at not just the last word, but the whole sentence or even the whole story?
And that’s how we start climbing toward modern AI.
The Jump to Neural Networks: When the Machine Begins to “Pay Attention”
Engineers began building systems that could look at more than one word at a time.
Instead of remembering only the last step, the computer started learning patterns across whole sentences.
Then came a huge breakthrough: the transformer model, which uses something called attention. It lets the computer look at all the words at once and figure out which ones matter most.
It’s like the difference between:
reading a story one word at a time with your nose touching the page
vs.stepping back and seeing the whole page, all at once
Suddenly, the computer could learn real patterns: how ideas connect, how questions work, how stories unfold.
This is the moment the guessing game grew up.
LLMs: Markov Chains on Superpowers
Modern large language models, like ChatGPT, still guess the next word just like a Markov chain.
But they do it with:
massive memory of patterns
millions of examples
the ability to see long-range connections
deep layers that learn meaning, tone, and structure
A Markov chain is like predicting one note of a song only from the previous note.
An LLM is like predicting the next note after listening to the whole song and every song ever made.
Same basic idea.
Radically different power.
Why This Matters for You
Here’s the quiet truth:
Every big invention even the ones that feel futuristic begins with a tiny, almost silly idea.
A simple rule.
A basic guess.
A tiny step.
If you ever feel like your ideas are too small, remember: today’s smartest AIs are built on the world’s simplest game.
Guess what comes next.
Here’s a version reframed for therapist–coaches, counselors, mentors, and advisors, in the same style profile:
A Quick Experiment You Can Try
Tonight, try this gentle exercise:
Write the first two sentences of a client scenario—something simple and familiar.
Pause and notice the response your mind predicts next.
Then ask an AI to generate its next step in the same scenario.
Compare the two—not for accuracy, but for perspective.
Notice how both you and the model are doing the same fundamental thing:
responding from learned patterns—just shaped by very different histories.
And maybe ask yourself:
What new possibilities open up when I can see my own predictive patterns alongside an external one?
What insight might grow from simply expanding the space of responses I consider?