It isn’t a graph cutting problem, graph cutting is just a simpler, special case of this more general cutting problem (h/t IBM Research). If you can solve the general problem you effectively get efficient graph cutting for free. This is obviously attractive to the extent you can do both complex spatial and graph computation at scale on the same data structure instead of specializing for one or the other.

The challenge with cutting e.g. rectangles into uniform subsets is that logical shard assignment must be identical regardless of insertion order and in the absence of an ordering function, with O(1) space complexity and without loss of selectivity. Arbitrary sets of rectangles overlap, sometimes heavily, which is the source of most difficulty.

Of course, with practical implementations write scalability matters and incremental construction is desirable.

Well, previously you said that it (presumably "it" broadly refers to spatial reasoning AI) is a "high dimensional complex type cutting problem".

You said this is obvious once explained. I don't see this as obvious, rather, I see this as begging the question--the research program you were secretly involved in wanted to parallelize the engineering of it so obviously they needed some fancy "cutting algorithm" to make it possible.

The problem is that this conflated the scientific statement of what "spatial reasoning" is. There's no obvious explanation why spatial reasoning should intuitively be some kind of cutting problem however you wish to define or generalize a cutting problem. That's not how good CS research is done or explained.

In fact I could (mimicking your broad assertions) go so far as to claim, the project was doomed to fail because they weren't really trying to understand something, they want to make something without understanding it as the priority. So they were constrained by the parallel technology that they had at the time, and when the computational power available didn't pan out they reached a natural dead end.