I beg to differ. It's complete hyperbole to suggest that the article said "it's the same problem as something in physics", given this statement:

     It seems that the bottleneck algorithm in GPT-2 inference is matrix-matrix multiplication. For physicists like us, matrix-matrix multiplication is very familiar, *unlike other aspects of AI and ML* [emphasis mine]. Finding this familiar ground inspired us to approach GPT-2 like any other numerical computing problem.

Note: Matrix-matrix multiplication is basic mathematics, and not remotely interesting as physics. It's not physically interesting.

Agreed.

Although, to try to see it from the author’s perspective, it is pulling tools out of the same (extremely well developed and studied in it’s own right) toolbox as computational physics does. It is a little funny although not too surprising that a computational physics guy would look at some linear algebra code and immediately see the similarity.

Edit: actually, thinking a little more, it is basically absurd to believe that somebody has had a career in computational physics without knowing they are relying heavily on the HPC/scientific computing/numerical linear algebra toolbox. So, I think they are just using that to help with the narrative for the blog post.

You are exactly right, after deep learning researchers had invented Adam for SGD, numerical analysts finally discovered Gradient descent. And after the first neural net was discovered, finally the matrix was invented in the novel field of linear algebra.