The Reddit post asking how to repeat order IDs and item numbers across multiple rows is a familiar frustration. It describes exactly the kind of task that makes people resent their spreadsheets: three criteria, one lookup, and a wall of manual work ahead. Our view is plain: this is a problem that should not exist in 2024, and the fact that it still does tells you everything about how far traditional tools have fallen behind.
The user in the post knows Power Query exists but cannot get it to behave. That is not a failure of effort. It is a failure of design. When a tool requires detailed written instructions just to repeat a column of values next to a column of items, the tool has stopped serving the user. The human outcome here is wasted time and rising frustration. The person asking the question is not looking for a gold star in data engineering. They want to get back to their actual job. That is the real metric. Every minute spent wrestling with lookup logic is a minute stolen from analysis, decision-making, or simply finishing work on time.
AI-native spreadsheets solve this by understanding intent, not syntax. Instead of asking a user to chain together INDEX, MATCH, or Power Query transforms, the system can recognize the pattern: take these order headers, expand them against these item numbers, and align the quantities. It is not magic. It is a better interface for the same logic. The difference is that the user describes what they need, and the tool handles the mechanics. That shift, from instructing a machine to collaborating with one, is where productivity gains live. For the person staring at the Reddit post, it means the answer is not buried in a forum thread. It is available in the moment they type the question.
The practical takeaway is this: if your spreadsheet makes you feel like you need a degree in database design just to unpivot a table, the tool is the problem. You are not asking for too much. You are asking the wrong tool. The next generation of data work should not require you to become an expert in the tool. It should require you to be an expert in your own data. That is the standard worth holding.