I might have missed the point but the Mito demo just shows importing a CSV file into a common table/grid of cells? Seems like a semi-shamless plug that 100% avoids the topic of the post: is there a better visual data model than a grid of cells?
The question of "what is a better visual data model than VisiCalc" is just one of the questions we can ask ourselves about how to create Excel 2.0. In the authors original post, they point out that Notion and Coda answer this question not with an evaluation on "cells" and the data model, but rather by extending the model to include a word processor. This isn't a purely visual change, but a functionality/integration one.
There are a bunch of different angles to consider the evolution of a spreadsheet, and, as I say in my response above, I personally think focuses on changes to the UI/display of data miss the point: what's missing in Excel 1.0 isn't a better display of data - IMO, it's giving the modern, powerful analysis tools that us programmers have access to the beginner-end of the programmer spectrum!
Different spreadsheet startups certainly have different theses on this. Subset [1] (the OP) seems to focus on side-by-side grids on an infinite canvas. Monday [2] (also referenced by OP) seems to focus on different "views" for a spreadsheet for project tracking, etc. Mito focuses on allowing you to integrate Python and spreadsheets as easily as possible. Clay [3] seems to focus on spreadsheet integrations into APIs/other data.
(Disclaimer: all the above are just my understandings of these tools, but I haven't used most of them directly mostly am just going of marketing materials... I highly recommend you check them out, though - they all look quite cool!)
My post def was a plug for Mito - I'll try and make my response to the post/thesis more clearly delineated in the future. I think this post is an awesome chance to get feedback on our spreadsheet thesis (and potentially hear back from OP on this thoughts!).
I don't feel that a grid of cells is the best visual representation for data. However, it is a natural representation for tabular data.
Tables are horrible for "visualizing". We didn't evolve looking at tables with hundreds of columns and millions of rows.
Once you learn some pivoting tools and charting, you can visualize things that you would never be able to find in a table of data. (Or if you did it would take a lot longer.)
(Sample size 1, but I teach Python, Pandas, and visualization (Jupyter w/ matplotlib/seaborn/bokeh). I've had clients tell me that one chart they came up with during a class on visualization more than paid for the training. They would have never seen that in the table of data. I've also found bugs in code by visualizing failure patterns.)
