This shift was more than a performance improvement; it was an architectural breakthrough. By enabling parallel computation across all tokens, transformers made it possible to train on massive datasets efficiently and capture dependencies across entire documents, not just sentences. In essence, it replaced memory with connectivity.
For engineering leaders, this was the moment machine learning architecture started to look like systems architecture which was distributed, scalable and optimized for context propagation instead of sequential control. It’s the same conceptual leap that turns a single-threaded process into a multi-core system: throughput increases, latency drops and coordination becomes the new design challenge.
Tokens, vectors and meaning
Think of a token as the smallest unit a model can process: a word, subworld or even punctuation. When you type “transformers power generative AI,” the model doesn’t see letters; it sees tokens such as [Transform], [ers], [power], [generative], [AI].
