SVMs in big data were already well liked back in 2010 as they could be scaled via map-reduce.

An issue in industry was that companies would not allow training on their data across customers. Understandably so, as SVM dimensions are readily interpretable given some thought. General purpose datasets were ill-suited too depending on the task.

So you'd start from scratch every time, annotate features like crazy, and often didn't have enough training data overall.

LLMs "solved" that by just ignoring the previous rules and training on everything, which lets the necessary world knowledge seep back in.

I'm sure we'll see many combinations of LLMs and regular machine intelligence in the near future.