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Why did attention and transformers replace RNNs for sequence modeling? Explain it conceptually.

The answer is not just 'transformers are better.' It is two specific wins: every position can attend to every other in one step (long-range dependencies) and the whole sequence trains in parallel instead of one token at a time. Plus the cost that buys, and where RNNs still make sense.

Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

The answer is not just 'transformers are better.' It is two specific wins: every position can attend to every other in one step (long-range dependencies) and the whole sequence trains in parallel instead of one token at a time. Plus the cost that buys, and where RNNs still make sense.

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FEDITOR'S NOTE

The screen is whether the candidate names the two distinct wins (parallelism during training and direct long-range access) and the price (quadratic attention cost), rather than chanting 'attention is all you need.' The reserved follow-up is the cost: attention is O(n^2) in sequence length, which is why long-context is an active engineering problem and why RNN-flavored ideas have resurfaced for very long sequences.

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