This thesis workflow is a perfect example of a common problem: the tool you know starts fighting you just when your work gets serious. The user here built a sensible system, original data, a condensed version, three neat sets per participant, and then hit the wall of Excel's autofill logic. It's not their fault. The software was designed for predictable, rectangular patterns, not the kind of structured grouping that real research demands.
The core issue is that Excel's autofill follows relative references, not semantic patterns. When you type `=HSTACK(A1:C1, A2:C2, A3:C3)` and drag down, Excel assumes you want the next row to shift every reference by one cell: `A2:C2, A3:C3, A4:C4`. It cannot infer that you actually want to skip three rows at a time because the formula language itself doesn't express that intent. The user tried adding more examples, rows 1 through 4 filled correctly, hoping Excel would recognize the three-row skip pattern. Sometimes it does, sometimes it doesn't, because the autofill heuristic is inconsistent with non-standard offsets. This isn't a user error; it's a limitation of a tool built for general-purpose tabulation, not for structured data assembly.
What this means practically is that the user is spending mental energy on wrestling a formula instead of analyzing their thesis data. Every manual copy-paste or failed autofill attempt is time taken from their actual research. For 150 participants, that's not just annoying, it's a productivity drain that compounds with each session. The solution isn't to learn more Excel tricks. It's to use a tool that understands data structure natively. An AI-native spreadsheet can accept a simple instruction like "stack every three rows of this column into a single row" and execute it without guesswork, because it processes intent, not just cell coordinates.
Our take is straightforward: if your data has a logical structure that your spreadsheet can't follow, the spreadsheet is the bottleneck. This user's approach, organized, systematic, already using functions like HSTACK, is exactly right. The problem is that the tool they're using treats their data like a grid of independent cells rather than a collection of grouped records. A smarter tool would let them describe the grouping once and apply it across all 150 participants in seconds. That's the shift we advocate for: stop adapting your workflow to a tool's limitations, and start using tools that adapt to your data's natural shape. For this thesis, the next step isn't more formulas, it's a better foundation.