why transformers scale

Before Transformers, the leading language models were recurrent networks that read text one word at a time, passing a memory along the chain. That sequential habit was their downfall: you cannot start word two until word one is done, so they were slow to train and prone to forgetting things from far back. Transformers threw out the chain and processed the whole sequence at once.

Two properties make Transformers scale. First, parallelism: because every position is computed simultaneously, training maps beautifully onto GPUs and TPUs that thrive on doing the same operation across thousands of items at once. Second, direct access: any token can reach any other in a single attention step, so long-range dependencies do not have to survive a long relay. Together these let practitioners pour in more data, parameters, and compute and reliably get better models — the foundation of scaling laws.

The catch is that self-attention cost grows with the square of the sequence length, which is why long contexts are expensive and why so much engineering targets that bottleneck. And scaling is not free understanding — bigger Transformers are more capable but no more transparent. Still, 'the same simple block, made bigger and fed more,' has carried the field remarkably far.