Breaking Down Transformers

A model that never went to school now writes code, explains quantum physics, and helps design new drugs. Yet under the hood, it doesn’t “think” in words at all. In this episode, we’ll pull apart the transformer engine that lets raw text turn into reasoning.

Seventy percent of today’s most powerful language models share the same core blueprint—and it didn’t exist before 2017. That blueprint is the transformer, and it quietly solved a problem that crippled earlier AI: how to keep track of meaning over long stretches of text without grinding computation to a halt.

In the last episode, we looked at how scaling up data, parameters, and compute makes models more capable. Now we’ll zoom in on *why* that scaling works so well for transformers in particular, and how a single architectural idea reshaped fields far beyond language.