Not to mention that the entire class of Markov Chain Monte Carlo techniques only form a subset of general uses for Markov chains.
Markov chains form the basis of n-gram language models, which are still useful today.
Markov chains are also the basis of the Page-rank algorithm.
Hidden Markov Models (which are just an extension of Markov Chains to have unobserved states) are a powerful and commonly used time series model found all over the place in industry.
In the pre-deep learning model Markov chains (and HMMs) in particular had very wide spread usage in Speech processing.
They are probably one of the most practical statistical techniques out there (out side of obvious example like linear models).
Not to mention, it was less than a decade ago that one could have said about neural networks "Decades pass and you realize they either have little to no application or are incredibly niche".
Markov chains form the basis of n-gram language models, which are still useful today.
Markov chains are also the basis of the Page-rank algorithm.
Hidden Markov Models (which are just an extension of Markov Chains to have unobserved states) are a powerful and commonly used time series model found all over the place in industry.
In the pre-deep learning model Markov chains (and HMMs) in particular had very wide spread usage in Speech processing.
They are probably one of the most practical statistical techniques out there (out side of obvious example like linear models).