Forget the hype. Here's what's really going on inside ChatGPT, Claude, and every AI that's taken over the internet — from the raw mechanics to the real-world impact.
Here’s the secret that powers every AI chatbot: all they’re really doing is predicting the next word. That’s it. When ChatGPT writes a poem or Claude explains quantum physics, neither one “understands” anything the way you do. They’re incredibly good at guessing which word should come next.
It’s like autocomplete on your phone, except trained on basically the entire internet and way more sophisticated.
AI looks at patterns and predicts what comes next. Can you?
Early AI language models were like reading through a straw — they could only look at the last few words at a time. That made them terrible at understanding context. Consider: “The trophy didn’t fit in the suitcase because it was too big.”
What does “it” refer to? You know instantly: the trophy. Because you paid attention to the right word. In 2017, researchers figured out how to make AI do the same thing. They called it the Attention Mechanism, and the AI architecture built around it became known as a Transformer — the technology behind ChatGPT, Claude, and basically every modern AI chatbot.
Click any word to see which others it “pays attention to.” Brighter = more attention.
The research paper was literally titled “Attention Is All You Need” — and it turned out to be true. Before this, AI struggled with long text. With attention, it could connect ideas across an entire conversation. That’s why the jump from old chatbots to modern AI feels so dramatic.
AI doesn’t read words the way you do. It breaks text into smaller pieces called tokens — the Lego bricks of language.
Type something and see it broken into tokens. Each color = a different token.
AI calculates a probability for every token in its vocabulary — typically 50,000+ options. Then it picks one.
Probabilities for what comes after “The weather today is really...”
Click resample to see different choices from the same probabilities.
Temperature controls creativity: low = predictable, high = creative but potentially weird.
Every time you send a message, the AI receives your entire conversation so far as one giant input. This is the context window — like a whiteboard. When full, older parts fall off.
Earlier messages fade as the window fills up.
Every conversation is independent — start a new chat and the AI has zero memory of previous ones.
Models are defined by parameters — tiny numerical knobs adjusted during training. GPT-2 had 1.5B. Frontier models today: trillions.
Each dot = 1 million parameters.
Training a frontier model can cost over $100 million in compute alone.
Show the AI a sentence with the last word hidden, ask it to predict, tell it the answer. If wrong, nudge its parameters. Do this trillions of times. The measure of “how wrong” is the loss.
Watch loss decrease as the model trains.
This is gradient descent — the model follows the “downhill slope” of its errors.
RLHF — Reinforcement Learning from Human Feedback. Human reviewers pick which of two AI responses is better. The AI learns to produce more of the preferred kind.
Prompt: “Explain gravity to a 5-year-old.”
You know how when you throw a ball up, it always comes back down? That’s gravity! The Earth is like a big magnet pulling everything toward it.
Gravity is a fundamental force described by Einstein’s general relativity as the curvature of spacetime, governed by the field equations.
Transformers are just one branch of Machine Learning (ML) — the science of getting computers to learn from data. Here are the key concepts.
Here’s how these concepts connect in a typical ML pipeline:
Click cells to label them as 🐱 Cat or 🐶 Dog. Watch the model’s confidence change.
Click once = Cat • Again = Dog • Again = Reset
AI isn’t just chatbots. It’s a family of technologies, each solving different problems.
AI can do incredible things — but “can” doesn’t mean “should.” AI needs guardrails, just like cars need road rules.
Click each principle to check it off.
How responsible AI applies to each solution type:
Like any powerful technology, AI can be misused. These aren’t science fiction.
The opportunities are staggering. AI is a multiplier for human capability.