Understand AI from the inside out

If you're reading this, you've probably already noticed that almost everyone around you is talking about AI — but very few of them can actually explain it.

And I don't mean explain it in a "well, it's basically pattern recognition" hand-wavy kind of way. I mean really explain it: where it came from, how it learns, what's actually happening inside a neural network, why AI agents are a fundamentally different kind of technology than a chatbot, and what the evidence actually suggests about where all of this is heading. Most people — including plenty of smart, experienced professionals — have a surface-level familiarity that crumbles the moment someone asks a follow-up question. I built this school because that gap between exposure and real understanding is one of the most consequential gaps you can close right now.

Here's what I believe about teaching hard things: the problem is almost never the student. It's the explanation. When a concept feels confusing, it usually means someone skipped a step, used jargon as a shortcut, or gave you the conclusion without the reasoning underneath it. My job is to give you the reasoning. So we start at the very beginning — Alan Turing, the 1956 Dartmouth Conference, the first programs that tried to reason — and we build forward from there. Every idea earns its place. Nothing lands from thin air.

The brain-vs-machine thread that runs through this curriculum is one I'm especially proud of. Understanding how biological neurons work — and then watching how McCulloch and Pitts tried to model them artificially, how the perceptron emerged, how backpropagation finally made training viable — gives you something most AI explainers skip: genuine intuition for why neural networks behave the way they do. You'll know where the human-brain analogy is precise and illuminating, and exactly where it misleads. That distinction matters more than most people realize.

By the time you reach the sections on AI models, agents, and the future, you won't be memorizing definitions — you'll be applying a framework. You'll be able to look at a new AI system, a new product, a new policy debate, and actually evaluate it rather than just react to it. That's the transformation I'm after: not a list of facts about AI, but a durable, flexible understanding that stays useful as the technology keeps evolving.

If you've ever wanted to be the person in the room who actually gets it — not the loudest voice, but the clearest one — this is the school I built for you. Come in curious. I'll handle the rest.