It depends on if you already had experience in using large Markov models for practical purposes.
Around 2009, I had developed an algorithm for building the Burrows–Wheeler transform on (what was back then) very large scale. If you have the BWT of a text corpus, you can use it to simulate a Markov model with any context length. It tried that with a Wikipedia dump, which was amusing for a while but not interesting enough to develop further.
Then, around 2019, I was working in genomics. We were using pangenomes based on thousands of (human) haplotypes as reference genomes. The problem was that adding more haplotypes also added rare variants and rare combinations of variants, which could be misleading and eventually started decreasing accuracy in the tasks we were interested in. The standard practice was dropping variants that were too rare (e.g. <1%) in the population. I got better results with synthetic haplotypes generated by downsampling the true haplotypes with a Markov model (using the BWT-based approach). The distribution of local haplotypes within each context window was similar to the full set of haplotypes, but the noise from rare combinations of variants was mostly gone.
Other people were doing haplotype inference with Markov models based on similarly large sets of haplotypes. If you knew, for a suitably large subset of variants, whether each variant was likely absent, heterozygous, or homozygous in the sequenced genome, you could use the model to get a good approximation of the genome.
When ChatGPT appeared, the application was surprising (even though I knew some people who had been experimenting with GPT-2 and GPT-3). But it was less surprising on a technical level, as it was close enough to what I had intuitively considered possible with large Markov models.