hash0

There is. Companies are anxious to be left behind and want to hire people who are confident in working with AI. And trust me, especially in ageing societies, even your colleagues who are demonstrably tech savvy are not necessarily inclined to working with AI, so you definitely have an advantage there.

I generally outline the problem first that I would like to solve, then ask for best practice approaches and industry standards that apply to situations. Next, I ask to summarize the pros and cons of the various possible solutions and how they apply to my particular situation.

Isn't there something similar available today, but with protein folding?

Honest question from an enthusiastic AI user:

"using AI consists of sending a message a waiting a long time (often an hour or more) before the AI finishes a task."

>>> What on earth are you prompting AI with that it takes so long to finish?!

Time Zones factor into that. They want to be able to operate 24/7 "follow the sun"-style, so they want to get people half way around the globe - who obviously can't come to the office.

Few companies these days actually offer full remote work in their own time zone - the allure of being able to "control" your employees by making them come into the office is just too strong and "productivity" is not something that can be easily measured, let alone discerned (not by the people who make the decisions, anyway).

Huh, now THAT adresses a real need: An off-work day between Saturday (aka "catch up with chores you had to neglect because you were working") and "have to dread/mentally prepare for the upcoming work week"-Sunday!

I'd call it "Funday", actually: A day for pure, unalloyed relaxation. Everyone would have time to visit friends and hang out with family.

In my line of work (IT Operations for a large international bank), we came up with a classification framework that tried to gauge information along two axes: Importance and Frequency of Use.

Importance is the more obvious one - there is critical info you absolutely need as a basis to understand the system, then there is also minor details. The difference is in an emergency, lack of the former will break your attempt to fix the system, lack of the latter will merely inconvenience you.

The Frequence of Use axis is the one that brought value to our operations when we introduced it. That's the question "How often will I need this info?". Regularly re-occuring, detailled procedures need to be accessible easily, so they go to a prominent Confluence page. But then there is information that you will hardly ever need, some arcanae about the system that only the old timers knew but that needed to be fixed in writing.

The most vexing bits of info are those that are highly important but are rarely ever needed. Do you plonk them into Confluence? Might not be the best idea because then you don't know how to quickly find it when need be. So we had an extra category for just that sort of thing which became our go-to info hub for all sorts of highly urgent but at first intractable issues with our system.

It does, thank you for your view on this!

Part of the answer to this conundrum might be the "lightswitch effect": Lightswitches are ubiquitous and easy to use, so you stop thinking about them, take them completely for granted and miss the bigger picture that even 150 years ago, this would have been considered not a small technical miracle!

LLMs seem to fall squarely into that category - the threshold to using them is incredibly low because you can "just talk to them" and they give back meaningful answers loaded with the sum total of human knowledge from the interwebs.

Agreed, but with one caveat: That is the book detailling the history of future science!

"Cosmos" by Carl Sagan - that book was a life changer for me and has been with me ever since.

If I may ask: In what areas have you found the most acceleration in productivity? For me as an enthusiastic amateur programmer (whose enthusiasm is, arguably, greater than my technical abilities) the only reliable and sustainable way to use AI in building software is to treat it like an exceptionally knowledgeable (though not perfect!), infinitely patient tutor that guides me through programming problems I run into. The minute I relinquish control and let it just write code for me I am doomed because AI cannot fix its own mistakes in anything of medium complexity or above - and then I have to dig down and try to understand code I haven't written which is, in my opinion, worse than having had to write it myself in the first place.

I wouldn't consider myself an SWE, just an amateur programmer, so this is an honest question: In your opinion, is there such a case where the usage of AI in software engineering has either sped up the dev time or improved product reliability (or even both)?

I started using LLMs in 2022, quickly turned away because the things obivously made up stuff I am a certified expert it. Back to Stack Overflow. Came around to LLMs in late 2025 again as they rolled it out in my workplace and quickly succumbed to them becoming my all-knowing, (almost) flawless, infinitely patient teacher - that was the last time I opened Stack Overflow. AI never tells me I'm dumb for asking a question, never passive-aggressively shoves genuine questions aside. And AI can just be as critical in dissecting my flawed approaches to problems as SO could be (which is good!) but without the toxicity.

Seems like the need for a support group depends on whether the recent advent of LLMs in your specific workplace has led you to become a Centaur ("I'm now a 10x SWE") or a Reverse Centaur ("this is rapidly sucking the joy this job once held for me"). So, yes, there is very definitely a need for this as there are definitely people seeing themselves cast as the latter.