For Kivy to stop being a niche UI framework with marginal adoption it must somehow tap into the central role of Python in the broader data science / machine learning universe.

Building data-centric cross-platform apps while staying (mostly [1]) within a single language ecosystem should be less friction and overhead than juggling multiple universes via API's, different runtimes and what not.

What kind of apps would benefit from this "single language" approach? For sure not the more open ended, exploratory data science type tasks. These are better delivered via notebook workflows, which, besides flexibility, enable better reproducibility and auditing. Also probably nothing that requires high performance interactive graphics.

But while not solving all UI problems for all people, there should be still plenty of relevant use cases where simplicity and fast prototyping give Kivy an edge when the task is to make algorithms and related tools available to non-technical users.

[1] ofcourse the actual number crunching might be done by yet another layer (typically C/C++) but that layer is essentially hidden from the data orchestation and UI integration that would be the Kivy app focus.