u/Artitecch

The question of whether AI is taking jobs or generating them is debated by everyone. However, what I find myself pondering is the impact on the social fabric surrounding work as AI performs more of it.

Much of the real networking, both at work and off, occurs as a result of conflict. Solving a problem together, seeking assistance, working long hours on a project. Little interactions that slowly add up to the real networks that people use afterward.

When the friction is eliminated by AI, the job is completed quicker. Do the dynamics of the relationships that usually occur within that tension go away as well?

It's already happening on a daily basis. Less need to seek assistance. Less chance of random joining together of people who wouldn't have otherwise met.

Not to say that there's nothing good. There was some friction that was not really necessary. Some of that was being social work, though, before no one realized it was missing.

Any of you have begun to experience this in your own life or business?

Been meaning to write this for a while. Not a hype post, just genuinely walking through what's helped versus what I tried and dropped.

I used to lose a lot of time staring at a blank page before starting anything creative, so now I throw a rough idea at AI first and react to what comes back instead of starting from nothing. It's also become my go-to for first drafts on the small daily writing I do, captions, notes, descriptions, since I'd rather shape the tone myself than write from zero every time. Beyond that, I think in a pretty scattered way, so feeding my messy notes and half-formed thoughts into AI and asking it to find the actual structure has saved me hours of untangling my own brain. And before I share anything that matters, I've started asking it to poke holes in it first, which has caught a few cringe moments before they happened.

None of this replaced the actual creative decisions, it just removed a lot of the friction that used to sit before the real work even started.

What's actually stuck for others doing similar work?

In most cases, marketing AI conversations center around production, the quicker the copy, the quicker the creative, the quicker the whatever. I believe this is where the real value is not even present.

Previously, five different campaign angles involved five investments, five briefs, five drafts, five internal reviews, etc. This is why so many teams chose the first “good” idea that came to mind, as they simply did not have the time or resources to look into more ideas.

I can now create 5 completely new angles, tones and hooks as I'm not hungry yet. It is not the end campaign that changed, it is that the final campaign reads better. More bad ideas are killed before they get to a client, or before they get to a budget meeting, and it's done quietly.

I guess that's partly why some marketers aren't seeing much of a difference in the quality of their output, but they know that their actual work certainly has. The enhancement is not expressed in the final asset. It's in all that stuff that was filtered out before it was there.

But with increased options, better decisions doesn't necessarily follow. If you don't have somebody who has a thought on how good it looks for the brand, then the more variations you have, the more time you're going to waste discussing it rather than shipping it.

I'm not sure if you've experienced this in marketing, but if you have, less about the fact that AI makes it better, more about the fact that it changes the number of ideas tested before something becomes real.

Been meaning to write this for a while. Not a hype post, just genuinely walking through what's helped versus what I tried and dropped.

I used to lose a lot of time staring at a blank page before starting anything creative, so now I throw a rough idea at AI first and react to what comes back instead of starting from nothing. It's also become my go-to for first drafts on the small daily writing I do, captions, notes, descriptions, since I'd rather shape the tone myself than write from zero every time. Beyond that, I think in a pretty scattered way, so feeding my messy notes and half-formed thoughts into AI and asking it to find the actual structure has saved me hours of untangling my own brain. And before I share anything that matters, I've started asking it to poke holes in it first, which has caught a few cringe moments before they happened.

None of this replaced the actual creative decisions, it just removed a lot of the friction that used to sit before the real work even started.

What's actually stuck for others doing similar work?

Tried voice mode for the first time properly last week instead of typing. Completely different experience.

Conversations felt less like "crafting the perfect prompt" and more like actually thinking out loud. Got messier, less polished questions, but somehow better answers.

Anyone else notice a difference between typing vs talking to AI?

One thing I've been observing in my workflow is that after I come up with an idea I know when to stop creating more ideas than I need to—sometimes it's a different skill than having good taste in the first place.

The ability to create another option is now costless, so there's no natural stopping point now. Cost was made the deciding factor before. Only you can make it now, and discipline.

I've found myself creating a sixth and seventh and eighth version of a thing that I wanted to make better, and it was just because I didn't want to miss out on something. Unfortunately, the diminishing returns began about version three, but I persisted, not for logic, but because it was my habit.

I believe this is the stealth mode of failure of the AI era for many. It's not bad taste, it's not bad prompting, it's just the lack of awareness about when more choices become counter-productive to time.

Anyone here has a personal rule for how many iterations to do? Or are most sticking to what feels intuitive, like I used to do until I realized what my own pattern was?

Something that's been sitting with me since talking about taste as the bottleneck: the people I see getting the best results aren't the most technical. They're the ones with the strongest, most specific opinions about their own field.

A mediocre prompt from someone with crystal clear standards consistently outperforms a perfect prompt from someone without a strong point of view. The AI can execute almost anything. It can't supply conviction about what "right" looks like for your specific context.

This feels uncomfortable to say out loud because it implies the skill gap isn't learnable through tutorials or prompt guides the way most people want it to be. You can teach someone prompting structure in an afternoon. You can't teach them to have strong, well-formed opinions about quality in their field that fast.

I wonder if this is why some experienced professionals adapt to AI tools almost instantly despite minimal technical interest, while some technically fluent people still get mediocre output. The opinion was already there for one group and missing for the other.

Curious if others have noticed this pattern in their own teams or fields.

Every benchmark, every comparison post, every "is X better than Y" thread measures AI in terms of output quality on a single task. Smarter answer, better code, cleaner copy.

But the way I actually use these tools day to day has nothing to do with single-task quality anymore. It's about how cheaply I can iterate. The unit that matters to me isn't "how good is this one response" — it's "how many attempts can I afford before I find the right one."

A model that gives a 7/10 response instantly but lets me iterate ten times in five minutes is often more useful to me than a model that gives a 9/10 response but takes longer to refine, or doesn't take correction well.

I don't see this measured anywhere. Every comparison is single-shot quality, when the real-world value is closer to iteration speed multiplied by correction-friendliness.

Wondering if anyone else has started judging tools this way instead of by raw output quality. Feels like the entire benchmarking culture around AI is built around the wrong question.

Most discussions I see compare model capabilities — which one writes better, codes better, reasons better. Fair enough, but I think it's missing where the actual bottleneck moved.

When I generate five versions of something now, the limiting factor isn't the AI anymore. It's me. Can I tell which of the five is actually good? Can I articulate why one direction works and another doesn't, fast enough to keep moving?

I've started noticing that people with strong taste in their field, even without technical AI skills, get dramatically better results than people who know every prompting trick but lack a clear sense of what "good" looks like for their specific context.

This feels like an inversion of the usual narrative. We talk about AI democratizing skill. I think it's actually doing the opposite in a subtle way — it's making taste the scarce resource, because execution stopped being the bottleneck.

Curious if anyone else in a creative or technical field has noticed this. Feels like the conversation should be shifting from "how do I prompt better" to "how do I develop better judgment," and almost nobody is talking about the second part.

Started doing this for anything important. Same prompt into two different tools, compare the answers.

Sometimes they agree completely. Sometimes wildly different, which is usually a sign I need to dig deeper myself instead of trusting either blindly.

Feels like a simple habit more people should have. Anyone else doing this already?

I've tested a lot of prompt styles. This one structure consistently gives me the best outputs across creative, technical, and mixed projects.

I call it the RCTX method:

R — Role: Tell it who to be

C — Context: Give it the situation

T — Task: Say exactly what you need

X — Exclusions: Tell it what to avoid

Here's how it looks in practice:

"You are a creative technologist with a background in UI design and AI tools. I'm building a personal brand around art and tech content for Reddit and social media. Write me 5 post ideas that would genuinely interest people who love both creativity and technology. Avoid listicles, avoid anything generic, and avoid AI hype language."

That one prompt gives you targeted, usable ideas in seconds.

You can plug any project into this structure:

Writing copy → R: copywriter, C: your brand, T: what you need, X: what to avoid

Designing concepts → R: art director, C: your brief, T: directions, X: clichés

Building workflows → R: productivity expert, C: your tools, T: the system, X: complexity

The more specific your X (exclusions) the better everything else gets.

Save this and try it on your next project. Would love to hear what results you get.

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