Transformers — The Architecture That Changed AI (Part 1 of 3)

Originally published on my blog. Cross-posted here with a canonical link.

In June 2017, a team at Google published a paper with a deceptively simple title: "Attention Is All You Need." Eight authors, fourteen pages, and one architecture that would go on to power GPT-4, Claude, Gemini, DALL-E, Stable Diffusion, AlphaFold, and virtually every breakthrough in AI since.

The Transformer didn't just improve on existing models. It replaced the entire paradigm. Recurrent neural networks, LSTMs, sequence-to-sequence models with attention — all of them became legacy architectures almost overnight.

This is Part 1 of a 3-part series. Here we cover the Transformer itself — the core architecture, the intuition behind each component, and why it scales so remarkably well. Part 2 will cover Vision Transformers (how this architecture learned to see), and Part 3 will cover Vision-Language Models (when AI learned to see and talk).

The Problem: Why RNNs Hit a Wall