This user's question reveals a common frustration: the gap between structured logic and spreadsheet usability. They have two parallel lists, one for lower bounds, one for upper bounds, and they need a filter that respects both simultaneously. That is a reasonable request, and traditional spreadsheet tools make it unnecessarily difficult. We think the real problem is not the data but the tool's inability to connect ranges in a meaningful, dynamic way.
In practice, what this user needs is a filter that understands relationships, not just individual values. Their lower-bound list maps categories like "NF" and "Under 18" to numeric thresholds, while the upper-bound list mirrors those categories with reversed logic. A standard filter expects a single column of criteria. When your logic spans two columns and requires conditional mapping, most spreadsheet software forces you to write nested IF statements, helper columns, or lookup tables. That works, but it is brittle. Change one bound, and the whole structure can break.
An AI-native approach changes this entirely. Instead of asking the user to build a manual bridge between the lists, the tool can infer the connection from the pattern. The AI recognizes that "NF → ≥ 0" and "NF → ≤ 1" are two halves of the same rule. It can create a single filter that says: for any row, apply the lower bound from one list and the upper bound from the other, and update both when the data changes. The user does not need to know how the mapping works, they just need to tell the tool what they want, and the logic follows.
This is the shift that matters. The user's question is not about a missing feature; it is about a missing mindset. Legacy tools treat filters as static, one-dimensional operations. An AI-driven spreadsheet sees filters as conversations between ranges, where the system adapts to the user's intent. For this user, the solution is not a complicated formula but a simple connection: link the two lists, tell the AI which column to filter, and let it handle the boundary logic. The result is a filter that stays accurate even when the thresholds change. That is the practical promise of this technology, not abstraction, but a direct answer to the question they actually asked.