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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

#171

Post by absynth »

PunchyHamster wrote: post #142

I'd argue 2 types of users are * People using it as a tool, aware of its limitations and treating it basically as intern/boring task executor (whether its some code boilerplate, or pooping out/shortening some corporate email), or as tool to give themselves summary of topic they can then bite into deeper. * People outsourcing thinking and entire skillset to it - they usually have very little clue in the topic, are int…

Other alternatives that aren't exactly "just as a tool":

* people who use it instead of search engines.

* people who use it as a doctor/therapist/confidant. Not to research. But as a practitioner.

There are others:

* people who use it instead of man pages or documentation.

* people who use it for short scripts in a language they don't quite understand but "sorta kinda".

Re: Two kinds of AI users are emerging

#172

Post by veunes »

This is the birth of Shadow AI, and it’s going to be bigger than Shadow IT ever was in the 2000s

Back then, employees were secretly installing Excel macros and Dropbox just to get work done faster. Now they’re quietly running Claude Code in the terminal because the official Copilot can’t even forma a CSV properly.

CISOs are terrified right now and that’s understandable. Non-technical people with root access and agents that write code are a security nightmare. But trying to ban this outright will only push your most effective employees to places where they’re allowed to "fly"

Re: Two kinds of AI users are emerging

#173

Post by jjav »

unlikelymordant wrote: post #157
jjav wrote: post #153

Earlier quoted context omitted.

> Isn't this true of any greenfield project? That is a good point and true to some extent. But IME with AI, both the initial speedup and the eventual slowdown are accelerated vs. a human. I've been thinking that one reason is that while AI coding generates code far faster (on a greenfield project I estimate about 50x), it also generates tech-debt at a hyperastonishing rate. It used to be that tech debt started to cat…

In your experience, can you take the tech debt riddled code, and ask claude to come up with an entirely new version that fixes the tech debt/design issues you've identified? Presumably there's a set of tests that you'd keep the same, but you could leverage the power of ai in greenfield scenarios to just do a rewrite (while letting it see the old code). I dont know how well this would work, i havn't got to the heavy t…

> ask claude to come up with an entirely new version that fixes the tech debt/design issues you've identified?

I haven't tried that yet, so not sure.

Once upon a time I was at a company where the PRD specified that the product needs to have a toggle to enable a certain feature temporarily. Engineering implemented it literally, it worked perfectly. But it was vital to be able to disable the feature, which should've been obvious to anyone. Since the PRD didn't mention that, it was not implemented.

In that case, it was done as a protest. But AI is kind of like that, although out of sheer dumbness.

The story is meant to say that with AI it is imperative to be extremely prescriptive about everything, or things will go haywire. So doing a full rewrite will probably work well, only if you manage to have very tight test case coverage for absolutely everything. Which is pretty hard.

Re: Two kinds of AI users are emerging

#174

Post by swordsith »

doom2 wrote: post #64

I guess this is as good a thread as any to ask what the current meta is for agentic programming (in my case, as applied to data engineering). There are all these posts that make it to the front page talking about productivity gains but very few of them actually detail the setup that's working for the author, just which model is best. I guess it's like asking for people's vim configs, but hey, there are at least a few…

In my opinion no frontier model is the best at everything, especially if you're having to catch it up with pre-existing information about your project or an esoteric scripting language, that being said, with Cursor you can try out all of the popular available models and get a feel for which do better with which tasks, in my experience - Codex is a okay model but use light thinking if you value your time, Gemini 3 flash is where its been at for me recently if I need to do big changes I go to that, And cursors model composer is good for making plans or doing refactors / making rules. Cursor gives you tools to make prompting feel like less of a repetition game, so you worry more about the task at hand and its been super efficient for me. I don't use proper version control so the fact it saves a history of every files dif's, and you can jump back easily in chats and regress the code base is the game changer.

Re: Two kinds of AI users are emerging

#175

Post by mike_hearn »

fortran77 wrote: post #87

I know it's fun to bash Microsoft, but--while Claude is better, Microsoft's Copilot is far from "awful". I've used it productively with the VS Code integration for some esoteric projects: PIC PIO programming and Verilog.

He's talking about the Copilot in other apps for non-programmers.

Re: Two kinds of AI users are emerging

#176

Post by chrisjj »

bitwize wrote: post #98
defrost wrote: post #12

The "upside" description: On the other you have a non-technical executive who's got his head round Claude Code and can run e.g. Python locally. I helped one recently almost one-shot converting a 30 sheet mind numbingly complicated Excel financial model to Python with Claude Code. Once the model is in Python, you effectively have a data science team in your pocket with Claude Code. You can easily run Monte Carlo simul…

The thing is, when you use AI, you're not really doing things, you're having things done . AI isn't a tool, it's a service. Now, back in the day, IBM designed and built an "executive data terminal". It wasn't really a computer terminal in the sense that you and I understand it. Rather, it was a video and two-way-audio feed to a room with a team of underlings, which an executive could ask for business data and analyse…

> be thinking twice before trusting anything an LLM tells me

You're too modest. You'd be thinking once.

However when the parrot is hidden in a shiny box made up to look like a regular, relatively trustworthy program...

Re: Two kinds of AI users are emerging

#178

Post by chrisjj »

decimalenough wrote: post #18

> I helped one recently almost one-shot[3] converting a 30 sheet mind numbingly complicated Excel financial model to Python with Claude Code. I'm sure Claude Code will happily one-shot that conversion. It's also virtually guaranteed to have messed up vital parts of the original logic in the process.

To be fair, he said almost one-shot.

It's like a CPU that's almost 100% reliable... in that it fails only once every 1 million clock cycles.

Re: Two kinds of AI users are emerging

#179

Post by kakacik »

PunchyHamster wrote: post #142

I'd argue 2 types of users are * People using it as a tool, aware of its limitations and treating it basically as intern/boring task executor (whether its some code boilerplate, or pooping out/shortening some corporate email), or as tool to give themselves summary of topic they can then bite into deeper. * People outsourcing thinking and entire skillset to it - they usually have very little clue in the topic, are int…

Second group are often the management decision makers, holding budgets, setting up 5-year plans etc. Don't underestimate them nor mock them, at the end its a disservice to all of us.

Re: Two kinds of AI users are emerging

#180

Post by veunes »

danpalmer wrote: post #29

I've noticed a huge gap between AI use on greenfield projects and brownfield projects. The first day of working on a greenfield project I can accomplish a week of work. But the second day I can accomplish a few days of work. By the end of the first week I'm getting a 20% productivity gain. I think AI is just allowing everyone to speed-run the innovator's dilemma. Anyone can create a small version of anything, while b…

All of this speedrun hits a wall at the context window. As long as the project fits into 200k tokens, you’re flying. The moment it outgrows that, productivity doesn’t drop by 20% - it drops to zero. You start spending hours explaining to the agent what you changed in another file that it has already forgotten. Large organizations win in the long run precisely because they rely on processes that don’t depend on the memory of a single brain - even an electronic one
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