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Two kinds of AI users are emerging

martinalderson.com

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Re: Two kinds of AI users are emerging

#41

Post by martinald »

AlotOfReading wrote: post #34
linsomniac wrote: post #23

Earlier quoted context omitted.

It depends on how easily testable the Excel is. If Claude has the ability to run both the Excel and the Python with different inputs, and check the outputs, it's stunningly likely to be able to one-shot it.

Something being simultaneously described as a "30 sheet, mind-numbingly complex Excel model" and "testable" seems somewhat unlikely, even before we get into whether Claude will be able to test such a thing before it runs into context length issues. I've seen Claude hallucinate running test suites before.

It compacted at least twice but continued with no real issues.

Anyway, please try it if you find it unbelievable. I didn't expect it to work FWIW like it did. Opus 4.5 is pretty amazing at long running tasks like this.

Re: Two kinds of AI users are emerging

#42

Post by bwat49 »

Havoc wrote: post #5

The copilot button in excel at my work can’t access the excel file of the window it’s in. As in “what’s in cell A1” and it says I can’t read this file. Not even sure what the point is then frankly. I’m happily vibe coding at work but yeah article is right. MS has enterprise market share by default not by merit. Stunning contrast between what’s possible and what’s happening in big corp

yeah I actually use AI a lot, but copilot is... useless. When microsoft adds copilot to their various apps they don't seem to put any thought/effort behind it beyond sticking a copilot button somewhere.

And if the copilot button does nothing but open a chat window without any real integration with the app, what the hell is the point of that when there's already a copilot button in the windows taskbar?

Re: Two kinds of AI users are emerging

#43

Post by majormajor »

martinald wrote: post #40
majormajor wrote: post #35

Earlier quoted context omitted.

I'm having trouble reconciling "30 sheet mind numbingly complicated Excel financial model" and "Two or three prompts got it there, using plan mode to figure out the structure of the Excel sheet, then prompting to implement it. It even added unit tests to the Python model itself, which I was impressed with!" "1 or 2 plan mode prompts" to fully describe a 30-sheet complicated doc suggests a massively higher level of gr…

No it didn't make a giant plan of every detail. It made a plan of the core concepts and then when it was in implementation mode it kept checking the excel file to get more info. It took around ~30 mins in implementation mode to build it. I was impressed because the prompt didn't ask it to do that. It doesn't normally add tests for me without asking, YMMV.

Ah, I see.

Did it build a test suite for the Excel side? A fuzzer or such?

It's the cross-concern interactions that still get me.

80% of what I think about these days when writing software is how to test more exhaustively without build times being absolute shit (and not necessarily actually being exhaustive anyway).

Re: Two kinds of AI users are emerging

#45

Post by aschla »

tharkun__ wrote: post #38
majormajor wrote: post #24

Earlier quoted context omitted.

One of the dirty secrets of a lot of these "code adjacent" areas is that they have very little testing. If a data science team modeled something incorrectly in their simulation, who's gonna catch it? Usually nobody. At least not until it's too late. Will you say "this doesn't look plausible" about the output? Or maybe you'll be too worried about getting chided for "not being data driven" enough. If an exec tells an i…

This is a pet peeve of mine at work. Any and I mean any statistic someone throws at me I will try and dig in. And if I'm able to, I will usually find that something is very wrong somewhere. As in, the underlying data is usually just wrong, invalidating the whole thing or the data is reasonably sound but the person doing the analysis is making incorrect assumptions about parts of the data and then drawing incorrect co…

It seems to be an ever-present trait of modern business. There is no rigor, probably partly because most business professionals have never learned how to properly approach and analyze data.

Can't tell you how many times I've seen product managers making decisions based on a few hundred analytics events, trying to glean insight where there is none.

Re: Two kinds of AI users are emerging

#47

Post by martinald »

smuhakg wrote: post #37

> On one hand, you have Microsoft's (awful) Copilot integration for Excel (in fairness, the Gemini integration in Google Sheets is also bad). So you can imagine financial directors trying to use it and it making a complete mess of the most simple tasks and never touching it again. Microsoft has spent 30 years designing the most contrived XML-based format for Excel/Word/Powerpoint documents, so that it cannot be parse…

Totally agree, though ironically Claude code works way better with Excel than I expected.

I even tried telling Copilot to convert each sheet to a CSV on one attempt THEN do calculations. It just ignored it and failed miserably, ironically outputting me a list of files that it should have made, along with the broken python script. I found this very amusing.

Re: Two kinds of AI users are emerging

#49

Post by wrs »

Some minor editing to how this would have been written in the mid-1980s:

“The real leaps are being made organically by employees, not from a top down [desktop PC] strategy. Where I see the real productivity gains are small teams deciding to try and build a [Lotus 123] assisted workflow for a process, and as they are the ones that know that process inside out they can get very good results - unlike a [mainframe] software engineering team who have absolutely zero experience doing the process that they are helping automate.”

The embedded “power users” show the way, then the CIO-friendly packaged software follows much later.

Re: Two kinds of AI users are emerging

#50

Post by with »

> The bifurcation is real and seems to be, if anything, speeding up dramatically. I don't think there's ever been a time in history where a tiny team can outcompete a company one thousand times its size so easily.

Slightly overstated. Tiny teams aren't outcompeting because of AI, they're outcompeting because they aren't bogged down by decades of technical debt and bureaucracy. At Amazon, it will take you months of design, approvals, and implementation to ship a small feature. A one-man startup can just ship it. There is still a real question that has to be answered: how do you safely let your company ship AI-generated code at scale without causing catastrophic failures? Nobody has solved this yet.

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