What is AI

We start with what you already think, not the final answer

If we just give you the explanation upfront, it’s easy to nod along and feel like you understand – right up until you try to apply it. Instead, we like to ask what you think will happen first and let you find out for yourself. Sometimes you’ll be right, but the moments where you're completely wrong are usually where the real learning happens. AI is full of things that sound obvious until you test them, so whenever possible, we let you play with the system before we dive into why it behaves the way it does. Make a prediction, break the system, watch what happens, and then we’ll explain the mechanics behind it.

Using AI isn't the same as understanding it

You can use a Large Language Model every day and still hold surprisingly inaccurate ideas about what’s happening under the hood, much like you tap on a glass screen dozens of times an hour without knowing how capacitive sensors read the electrical charge of your fingertips. But if you're here to actually understand AI, eventually we have to look underneath the surface. That also means recognizing that AI didn't start with ChatGPT. Modern LLMs are fascinating, but they're just one chapter in a much larger story that spans symbolic reasoning, expert systems, neural networks, reinforcement learning, and a few famous dead ends. We’ll study the tools you use today, but we’ll also look at what came before them, why early approaches failed, and what everyone got wrong along the way.

We don't want you to memorize concepts – we want you to notice patterns

Our goal isn't to help you pass a test; it's to change how you look at technology. After walking through these lessons, you’ll start seeing the shared patterns across recommendation engines, vision models, chess AIs, and autonomous agents. They all have different histories and failure modes, but suddenly they start making intuitive sense together.

You don't need to learn calculus, memorize complex equations, or call yourself a "prompt engineer" to get value out of this. You just need enough curiosity to look at a system and ask, "Wait–is that actually how it works?" That’s where we start.