You’re Learning AI Backwards. Here’s Why You’re Stuck.

Let me guess. You’ve watched the transformer tutorials. You’ve prompt-engineered your way through GPT-4. You’ve fine-tuned a model or two. And yet — when someone asks you why attention works, or what a language model is actually doing when it predicts the next token, you freeze.

You’re not alone. Most people building with AI today are operating a machine they fundamentally don’t understand. And the reason isn’t that they’re not smart enough. It’s that they started in the wrong place.

The deepest insights into LLMs don’t come from the latest transformer paper. They come from questions philosophers were asking 2,500 years ago.

That sounds absurd, right? In an industry obsessed with speed, who has time for epistemology? You’ve got products to ship. But here’s the paradox: the people who actually understand AI — not just use it — are the ones who went back to first principles. And first principles don’t start with “Attention Is All You Need.” They start with: What is meaning? What is knowledge? How does language relate to thought?

I saw this firsthand. The most capable AI engineers I know aren’t the ones who memorized PyTorch APIs. They’re the ones who can tell you why symbolic logic failed, why connectionism won, and what that tells us about the limits of what neural networks can ever do.

That’s why I built a free curriculum that teaches AI from the beginning — not from the flashy end. It goes in chronological order: philosophy, then logic, then mathematics, then computing, then machine learning, then deep learning, then LLMs. Each layer builds on the last. By the time you reach transformers, you don’t just know how they work — you know why they work, and where they’ll break.

Most courses teach you to drive the car. This one teaches you how the engine was invented — and why it’s shaped the way it is.

Think about it. The entire field of machine learning is a footnote to a debate between rationalists and empiricists. Neural networks are a computational answer to a question Locke and Hume were arguing about in the 1700s. LLMs are, at their core, a radical claim about language and meaning — one that Wittgenstein would have had strong opinions about.

If you don’t know that history, you’re not building on solid ground. You’re building on sand and calling it a foundation.

Here’s what happens when you learn AI backwards — starting with transformers and working backward only when forced to: you can use the tools, sure. But you can’t reason about edge cases. You can’t predict failures. You can’t distinguish a genuine breakthrough from a clever hack. You’re a passenger, not a driver.

The people who’ll shape the next decade of AI aren’t the ones who learned fastest. They’re the ones who learned deepest.

I get it. Depth is slow. Speed is seductive. The industry rewards shipping, not understanding. But here’s the thing: the gap between “can use GPT-4” and “can build the next GPT-4” is not a gap of tools. It’s a gap of foundations. And foundations take time.

So here’s my challenge: stop optimizing for speed. Spend a few weeks with Plato. Then Frege. Then Turing. Then Rosenblatt. Then Rumelhart. Then Vaswani. By the time you reach the present, you’ll see the entire field not as a series of breakthroughs, but as one long conversation about what it means to think.

That conversation started long before computers existed. It’s time you joined it.