What's Actually Happening

In November 2022, OpenAI released a chatbot called ChatGPT. Within two months it had 100 million users — the fastest product adoption in history, outpacing Instagram, TikTok, and every app that came before it. People weren't just curious. They were using it for work, for school, for things they'd never thought to ask a computer before.

If you're reading this because something changed at your job, or because a friend won't stop talking about AI, or because you just want to understand what's going on — you're in the right place.

What "Generative AI" actually means

Most of the AI you've encountered before was narrow AI — software trained to do one specific thing. Spam filters, recommendation engines, fraud detection, autocorrect. It classifies, ranks, or predicts, but it doesn't create.

Generative AI is different. These models generate new content: text, images, code, audio, video. They don't retrieve answers from a database — they compose them, on the fly, in response to your input. That's the shift. The same underlying technology that writes a poem can also draft a legal brief, explain a blood test, debug code, or hold a realistic conversation.

The model powering ChatGPT is called a large language model (LLM). We'll look at how they actually work in the next module. For now, the key idea: this isn't a fancier search engine. It's something structurally new.

It's not just chatbots

The public conversation focuses on ChatGPT and Claude, but the technology has spread across every medium:

The same architectural breakthrough that made conversational AI viable also made generative image, audio, and video models possible. This isn't a series of separate inventions — it's one wave moving across every medium at once.

Why a course for non-technical people

The people most affected by a technology are often the last to get a clear explanation of it.

The technical audience got early access: researchers, engineers, product managers who spent years watching this coming. They had context. The rest of us got a tool dropped in our laps, surrounded by breathless press coverage, corporate announcements, and genuine experts disagreeing about what it all means.

The next lesson looks at how we got here — and why this wave feels different from the AI hype cycles that came before it.