A quick note on how this was made: Yes, I see the irony of using AI to help draft a post about the useless use of AI :D. However, instead of lazy one shot prompting, I co-wrote this piece alongside Gemini using recursive chain of thought and custom voice constraints to iterate on my rough thoughts and refine the structure (including a strict prompt rule to ban hyphens so I do not look like a bot XD). Intentional collaboration is the exact opposite of what I am complaining about below, and the core arguments, frustration, and perspective are 100% human.
I was looking at how people use AI in their daily workflows lately, and it turns out that a huge chunk of our token expenditure is going toward tasks that do not actually save any time (or worse, tasks that had no business being automated in the first place).
I mean, leaving aside the funny side of AI generation (like absurd memes, cursed images, or just poking around with prompts for fun :D), there is a growing trend of running inference on things that are simply not worth the effort, or that become completely meaningless once you strip the human out of them.
What I mean by “useless” (and what I do not)
Of course, I am not talking about using AI to automate repetitive infrastructure work, parse massive logs, or speed up a real technical bottleneck. That is what automation is actually for! What I am talking about is the performative, low value use of generative models where the tool adds zero net value to the world.
Basically, if you look closely at how people interact with LLMs and image generators every day, this useless usage usually falls into two buckets:
1) The Prompt Engineering Overhead: Have you ever watched someone spend fifteen minutes tweaking a prompt, on a prompt per prompt basis, just to write a three line email, a casual Slack update, or a happy birthday message? It turns out that by the time you finish giving the model context, regenerating the output because it sounds too robotic, and deleting words like “delve” or “testament,” you have spent triple the energy it would have taken to just type what you actually meant. We are turning a 45 second human interaction into a debugging session.
2) The Zero Sum Inference Loop: Person A writes three rough bullet points and asks an LLM to turn them into a five paragraph, overly polite corporate email. Then, Person B receives that five paragraph email, refuses to read all that fluff, and asks their LLM to summarize it back into three bullet points. Nobody gained any new context, no real communication happened, and we just burned compute and tokens to maintain the statu quo of corporate politeness. Anyway, going back to the point:
The cost to human creators
If this were just about people wasting their own time in a chat window, it would merely be ironic. However, when we apply this same effort avoidance mindset to creative work, the collateral damage hits actual human creators.
Again, leaving the memes aside (and the cute AI generated cats), the real problem starts when people use AI to generate “good enough” creative filler. Think of blog headers, indie book covers, event flyers, newsletter illustrations, or background tracks generated by a model just because someone does not want to pay an artist or spend time curating something real. In my opinion, this creates three massive problems for the creative ecosystem:
1) It kills the entry level commission:
Historically, small commercial gigs (designing a poster for a local event, illustrating a single article, or writing short copy) were the exact stepping stones that allowed emerging artists and writers to pay rent while developing their voice. When people treat those small tasks as friction to be automated away with a single prompt, they pull up the ladder for working creators.
2) It mistakes the output for the art:
There is a huge misconception that art is just the final image file or the final block of text. It is not. What makes a piece of writing or an illustration resonate is the human context behind it, including the lived experience, the intentional constraints, the happy accidents, and the specific point of view of the person who made it. When you replace that with a statistical average of scraped training data just to fill empty space on a page, you are not making art. You are just generating wallpaper.
3) It floods discovery channels with noise:
Because generating this kind of filler takes almost zero effort, it floods the exact same platforms where human creators are trying to share their work. It becomes harder and harder for intentional, human made craft to be found when it is buried under an avalanche of automated content that not even its prompter cared enough to make by hand.
The takeaway
For decades, the whole promise of technology and automation was that machines would handle the boring, soul crushing drudgery so that humans would finally have the free time to write, paint, think, and connect with each other. Instead, we somehow flipped the script: we are still doing the dishes and dealing with bureaucratic admin, while outsourcing our personal messages, our rough drafts, and our visual culture to a model.
Basically, just because we can throw AI at a task does not mean we should. Using AI intentionally means knowing where automation actually solves a hard problem, and knowing where the human effort, the imperfection, and the creator’s craft are the whole point. If we outsource our own voice and bypass human artists just to save five minutes on things that were never supposed to be optimized... what are we even saving that time for?
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