I think the reason is in the general ballpark of people throwing LLMs at a huge variety of problems and being too slow disqualifies them from a bunch of things.

Now there's a new training-free thing that is fast enough to be useful on a new class of problem.

If you have a little data and can ask a frontier LLM to train a model, you can probably beat it on average for a specific task.

But... This is the case with LLMs too.