The other aspects is the new generation doesn't understand it. During interviews in marketing but also for a medical field expert, I often ask "make a small budget so you'll see how your bonus will work", and even the PhD, which is the top academic level, couldn't use a basic formula. How do they deal with data during a PhD? "In Prism", she said.

Same for marketing students and, although it's a lower academic level, they still need Excel for prospect lists.

Recent grads at all levels always suffer from this problem. The issue is they’ve not needed to use it with enough frequency in their academic lives to build the knowledge or muscle memory. Things have improved a lot in past 10 years or so, but it’s even true with many Finance and Accounting grads (that’s who I hire). Many of them just have never seen real world business data and working with it so different than the contrived information they had seen in coursework. That said give them $15 to spend on Udemy. Then, give them a few assignments, with real world business data. And most people pick it up pretty quickly.

A PhD is not for teaching about specific software tools, and hopefully even less so about legacy software from the 80's.

If you want people to be able to use Excel or service a mechanical typewriter, you need to spend extra effort to teach them.

I'm one of those. Excel's ease of use isn't relevant if you already know how to use Python / Jupyter / DuckDB / whatever.

The mental model of describing a series of reproducible transformations on normalized tables is more intuitive to me.

Excel lets you do too many things. As a result, I mostly see spreadsheets that aren't much more machine-parsanle than a word document.

We had to write a score-table at a party lately, and a while ago, and I managed to get it working with DataSpell faster, than it took us to figure out how Google Sheets stupid date-parsing works.

Sounds like a fun party.

Even as a programmer I find Excel pretty useful for anything involving numbers. The fact that everything is reactive allows for very fast iteration when testing stuff.

I wouldn't necessarily want to maintain a really complicated spreadsheet but I stop using spreadsheets once it gets anywhere near "complicated". It's still valuable for what I use for.

I work at an airline where Claude is replacing data analysis tools like excel, data visualization tools like power bi, all that's left is dynatrace because it does it all automatically like Claude does. Many bespoke data processing or data analysis tools for non programmers are being replaced by Claude suscriptions, like excel, power bi, spss, tableau, those are the most used I think. No Python data analysis nor big data tools like pandas, numpy, apache superset are being replaced though

I would argue using Claude in replacing a spreadsheet is potentially more of a tech debt than the spreadsheet itself. Now if you would use Claude to build a point application that performs the same standard analysis it might be better, but only marginally so.

...an with Claude/LLMs you still cant be sure if there is some where a hallucinated mixup in some numbers, yielding wrong results:

If I select whatever datarange in Excel, I know I can rely on the numerical output.

Though, Claude became much better with this

Their only complaint with Claude has been hallucinations when using it for things like remembering business rules, but that's an easy fix since it has no Access to them and the company is large enough to be different from other companies which causes the hallucinations

I love Claude and use it for many things, including analyzing and manipulating Excel files. But I’m not convinced that replacing spreadsheets with Claude is the right approach.

Over the years, we’ve seen many platforms promise to replace spreadsheets for a particular use case or industry. Businesses adopt them and push users away from Excel, but in many cases, those same users eventually find their way back to Excel to do the exact same work.

My concern is that we may end up creating another dual-system maintenance problem, where some people use AI and others continue using Excel for the same business process. Over time, the two versions inevitably diverge, potentially creating more problems than they solve.

Excel is much stickier than most people realize. I think the more interesting opportunity is figuring out how AI and Excel can work together, rather than trying to replace one with the other.

So, Excel is just the poor man's shadow IT? I like that picture.

By the time it takes to make a custom filtered table non extensible UI for an api, the excel guy is finished three times over

I can’t count the number of demos I’ve seen that were “small team, multiple sprints” to produce something that could have been done in a lazy afternoon in excel by a single person.

It’s a common pattern in enterprise IT to take a useful spreadsheet and turn it into an application. Without fail, they turn into a forever chase of excel features re-implemented (and poorly) on some front end. Entire teams dedicated to features that would be two clicks away in excel ootb.

Indeed. Sometimes it's hard not to think about gaslighting in this field.

Yes, I have already three such projects on my CV, failed attempts to replace Excel.

The root cause for failure is always the same, not understanding all use cases for 100% of the department users instead of only the representative group present during UI/UX sessions, coupled with the human natural resistance to change.

I mean spreadsheets are amazing, but excel is still a shitty piece of software (in my books)

I recall excel being decent once upon a time, back in the early 2000's, before the ribbon interface was added.

The introduction of the Office ribbon was one of the biggest improvements in software that I have ever experienced.