Spreadsheets have a way of ruining a perfectly good afternoon. You ask an AI to build one, it spits out a file full of mystery numbers, and half the time you can’t even open the thing. Then you give up and rebuild it by hand anyway.
That’s exactly the frustration I saw tackled in a brilliant LinkedIn post, and I had to share it with you. The author ran a head-to-head experiment that most of us would never have the patience for. They tested 11 different AIs to build one single spreadsheet, and the results were rough. Microsoft Copilot failed. Gemini failed. Grok failed. The expert noted that most of these tools couldn’t even open the file they’d just created.
Then they tried Claude Cowork. The difference was night and day.
According to the original poster, Claude Cowork built a working model with 6 tabs and over 700 formulas, while the others tripped over the basics.
Why most people get AI spreadsheets wrong
The creator pointed out the trap most of us fall into. We type something lazy like “Make me a spreadsheet” and hit enter. The AI then invents numbers out of thin air, hardcodes them, and buries them deep inside formulas you can’t trace. When something looks off, you can’t find the error. So you scrap it and start over manually.
I was nodding along the whole time reading this, because I’ve absolutely done that. The fix the expert shared is less about a magic tool and more about a smarter process.
The step-by-step playbook
Here’s the exact sequence this savvy professional laid out. Each step matters, so I’ll add a quick note on why.
- Download the Claude app for free. No fancy setup, just the standard app.
- Click the “Cowork” tab. It sits between the Chat and Code tabs, and it’s the workspace that makes the file-building possible.
- Select your folder, then pick “Opus 4.8” and set “Effort high.” The high effort setting is what gives you the deeper, more careful build.
- Connect Google Drive using the “+” button under Connectors. This is the trick that lets the finished sheet open live, instead of vanishing into a download you can’t locate.
- Paste your data and describe the sheets you need. Be specific about the tabs and structure you want.
- End every single prompt with one specific line.
That last step is the heart of the whole method. The mind behind this post says to close every prompt with this exact instruction:
“Before building, list your top 10 assumptions so I can sanity-check them, then execute.”
The creator called that line the whole trick, and I have to agree. It flips the power dynamic. You control the AI, not the other way around. Instead of the model running off and guessing, it pauses and shows its work first.
Three things the author didn’t expect
What I love about this post is that the expert was honest about being surprised. Here are the three things that caught them off guard.
- It defends its numbers before it builds. The AI lists its 10 assumptions, and you get to challenge them. Every input lives on a dedicated “Assumptions” tab, fully editable. Nothing gets buried inside a formula you can’t follow.
- It builds board-ready models from scratch. The original poster gave it a prompt like “12-month revenue forecast, 4 service lines, Base/Bull/Bear toggle” and it just built the whole thing. Funnel, P&L, Dashboard, Scenarios. It doesn’t waste time explaining what a forecast is. It builds one.
- It opens straight into Google Sheets. No more “done!” message attached to a file you can never find again. You click Drive and the entire model opens up, live and working.
Why this matters
The line from this post that really stuck with me was about expertise. The author said that 41 years of Excel experience is no longer a moat. That’s a bold claim, but their test backs it up. Claude built in roughly 7 minutes what used to eat up a full day of manual work.
I think the bigger lesson here goes beyond spreadsheets. The real shift is in how we talk to these tools. Asking for assumptions first isn’t just an Excel hack. You can bolt that same sanity-check line onto almost any complex request, whether you’re building a budget, drafting a project plan, or mapping out a content calendar. It forces the AI to be transparent before it commits.
It fits a trend I keep seeing across the industry. The people getting the most out of AI aren’t the ones with the slickest one-line prompts. They’re the ones who build a little verification into the process and refuse to take the first output at face value.
If you regularly wrestle with spreadsheets, this approach is worth a real test run. Try the assumptions line on your next build and see how much cleaner the result comes out. Big thanks to this LinkedIn creator for running the experiment so we don’t have to. Check out the full LinkedIn post for the complete breakdown and their exact prompts.