Apoya a Attention, After All?

Will Transformer-like models still lead most NLP benchmarks in 2027? Attention, After All? is a small, independent field guide to that question: the original wager, the architectures challenging it, and the definitions that change the answer.

Explore the countdown, compare architecture definitions against published results, browse the research timeline, and test your instincts in the quiz. Sources and evidence dates are part of the guide.

If you find it useful, a coffee helps support the research, writing, and upkeep. The guide is free to read and explore, and support is entirely optional. It doesn't buy influence over the evidence or conclusions.

This project is independent and unaffiliated with either side of the original wager. Contributions support this site; they are separate from the charitable donation described in the bet.