Attention Is All You Need

Just finished reading Attention Is All You Need and I genuinely can't stop thinking about it. I knew Transformers power modern LLMs, but reading the original paper felt different. The craziest part? The authors looked at a world dominated by RNNs and basically said: "What if we remove recurrence entirely?" And somehow that worked. No recurrence. No convolutions. Just attention. The paper isn't even trying to be flashy. It's mostly a series of elegant engineering decisions: Scaled Dot-Product Attention Multi-Head Attention Residual Connections Layer Normalization Positional Encodings Massive Parallelization Individually, none of these ideas feel magical. Together, they changed AI. One thing that really stood out to me was the discussion about path lengths between tokens. In an RNN, information might need to travel through many sequential steps. In self-attention, every token can directly interact with every other token in a single layer. Such a simple idea. Such massive consequences. It's funny reading this paper in 2026 knowing that GPT, Claude, Gemini, Llama, Mistral, DeepSeek, and basically every major LLM can trace their roots back to these 15 pages. Sometimes progress isn't adding more complexity. It's removing the thing everyone assumed was necessary. Attention really was all they needed. Question: What's your favorite ML paper of all time and why? Do you think the Transformer will eventually be replaced, or are we still in the early chapters of its story?