In 2017, a team at Google published a paper with a title that turned out to be accurate: "Attention Is All You Need." The architecture they described didn't just improve on what came before. It replaced it, and within a few years it had become the foundation of nearly every major AI system in use today.
To understand why, it helps to know what came before.
The dominant approach to language modeling before transformers was a family of architectures called recurrent neural networks. These processed text sequentially, one word at a time, passing information forward as they went. The problem was that by the time the network reached the end of a long sentence, the information from the beginning had been compressed and partially lost. Long-range relationships were hard to capture. And because the processing was sequential, it was slow to parallelize, which put a ceiling on how large and capable these models could get.
The transformer solved both problems at once. Instead of processing words in sequence, it processes all of them simultaneously, using a mechanism called attention to weigh the relevance of every word to every other word in the input. Nothing gets lost to distance. The model can attend directly to any part of the input, regardless of where it appears. And because the computation is parallel rather than sequential, it scales. You can make the model bigger, train it on more data, and the results keep improving in ways that sequential architectures couldn't match.
That scalability is what unlocked the capabilities that define modern AI. GPT, Claude, Gemini, and virtually every other large language model is built on the transformer. The details of how it works are in the article ahead. The thing to carry into it is this: the transformer wasn't just a better tool for the same job. It was a design that made a different class of capability possible.
