This Reddit user's problem is exactly the kind of puzzle that makes traditional spreadsheets feel like a dead end. They have 300 rows of people, five choices per person, and a simple question: who shares the most in common with whom? The formula they tried works for comparing one person to the rest, but scaling that across every row becomes a manual grind. That's not a user error. It's a tool limitation.
What this reveals is a gap between the data we have and the insights we actually need. The user isn't asking for a faster way to copy formulas. They want to see the hidden structure of their dataset, the relationships between people, the clusters of shared preferences, the outliers who match no one. That's not a spreadsheet problem. That's a pattern-recognition problem. And pattern recognition is exactly what AI-native tools are built to solve.
Imagine a system where you don't write a formula per row. You simply ask: "Show me which people have the most overlap in their choices." The AI understands the structure of the data, names in column A, choices in columns B through F, and returns a ranked list of pairwise matches. It identifies the person who shares all five items with another, the ones who share four, and the ones who barely share any. No dragging formulas. No manual comparison. The insight is surfaced, not excavated.
This is the shift we believe in. The goal isn't to make spreadsheets slightly easier to use. It's to make them capable of answering the questions you didn't know you had. The user in this thread solved their problem with a workaround. That's resourceful, but it shouldn't be necessary. A tool that truly empowers your data journey should let you ask the question directly and get the answer. That's the future we're building toward, and it starts by recognizing that the real work isn't writing formulas, it's understanding what your data is trying to tell you.