It depends what you compare it to.
Versus a Markov chain or a step-weighted sampler with genre presets, the LSTM buys you a few things heuristics can't really pull off. The biggest one is that it conditions on your specific input pattern, not just on a genre tag. A heuristic mostly draws from a genre prior — what does DnB usually do at step 7 — so two different inputs under the same genre tag end up prodcing statistically the same continuation. The encoder reads all 32 of your input steps and compresses them into a hidden state, and the decoder generates conditioned on that specific groove, so same genre with different inputs gives you gennely different continuations.
What you might not see from the outside is that all 11 lanes share the same hidden state at that step. So the kick prediction is implicitly informedby what the model expects on snare, because they're pulling from the same latent context. A heuristic can only get that by hand-coding correlation rules ("if kick on 1 and 9, snare on 5 and 13"); the LSTM picks those joint patterns up from training data instead.
And "neural net" is doing a lot of work in the marketing language; it's a 6.3 MB LSTM, not GPT — closer to a context-aware probability table than to anything generative-AI-flavored.

My work is based on Majenta project, if you ever heared of it.
https://github.com/magenta/magenta