Why AI Slop Is a Human Problem

I’ve become increasingly frustrated by AI slop.

Not because I hate AI. Quite the opposite. I’m a big fan and advocate of it. For example, I recently built Slugged, a small ASP.NET Core library, using AI. I made the architectural decisions and spent a lot of time working through designs with it. Agents wrote the code and tests while I was away. Afterwards, I reviewed all of it until I was happy to publish it. I’d estimate the work at a week or two. It took three days, with everything else I had going on. I use AI extensively for my work and personal life.

Which is why AI slop bothers me so much.

I’ve seen Instagram stories with five different font pairings and awful paint strokes on top of a “too perfect” image.

I’ve watched generated videos of characters speaking where their voice changes with every camera cut.

I’ve reviewed PRs where the AI wrote 200 lines of code where 12 lines would do, along with dozens of useless comments.

It’s tempting to look at all this and decide that AI is the problem. Plenty of people have. They scroll past anything they suspect was made with AI, or they write the whole technology off. I think that’s an overreaction, and it gets the cause backwards. It lets the human creator off the hook.

In this article, I’ll argue that slop is a human problem, not a tool problem, and that the fix is better judgement and expertise, not abandoning AI. I’m only going to address slop here. Copyright, energy consumption, and AI’s impact on jobs are separate points that deserve their own discussion.

What Is AI Slop?

Scroll through social media comments and you’ll see people label almost any use of AI as “slop”. Used that broadly, the word becomes meaningless, so it’s worth defining precisely.

Slop is low-effort content published without anyone checking whether it’s any good. It existed long before AI. AI slop is the AI-generated kind where output is published without the creator applying real judgement to it. Nobody checked whether it was accurate, useful, or good, or the person who checked didn’t have the expertise to tell. I’ll use “slop” and “AI slop” interchangeably in the context of this article.

AI makes generating outputs cheap. The person using it still decides what to keep, discard, fix, or ultimately ship. Skipping that work, or ignoring the common patterns AI repeats in its outputs like the ones I describe above, results in slop.

The Trap

Slop is published because humans fall into the trap of thinking AI can do everything for you, including thinking.

It’s easy to fall into that trap, especially given how fluent and confident a model’s output can be. A generated article has clean headings and paragraphs. Generated code usually compiles and runs. A generated image looks polished and complete. Fluent output reads as competent output, so the review step starts to feel optional.

Writing a draft or building a feature used to take long enough that you’d review and reshape work as you went. Now the first version arrives in seconds, and reviewing it is the only step that still takes real effort. That makes it the easiest step to skip.

Better models won’t change this. A model could one day produce flawless output, but if that output misses the original requirements and you publish it without checking, it’s still slop. Those requirements come from you. They cover what you’re trying to say, who you’re saying it to, and what the result needs to do. Models also state wrong things with the same confidence as right ones, so you still have to check whether the result is true. You can delegate the typing and the first draft, but not the decisions about what to build, how to build it, what’s accurate, and what’s worth someone’s time.

This is where expertise matters. Reviewing output takes the same skill as producing it manually. Someone who knows the right solution spots a bloated or wrong one right away. Someone who lacks that expertise will see the output and approve it as-is.

The less expertise you have in an area, the harder it is to spot poor output, and the more tempting it is to trust it. AI doesn’t make expertise redundant. Expertise is what separates good output from slop.

Why People Dismiss All AI Output as Slop

Content consumers are the ones that feel the impact of unreviewing AI-generated outputs. When you’re constantly bombarded with low-effort output, it’s understandable that people dismiss anything made with AI. Everything starts feeling fake.

But that reaction is a shortcut, and it presents two problems. First, it judges the work by how it was made instead of how good it is. Someone who dismisses everything AI-generated treats a carefully edited article the same as an unedited one. Second, it’s built on a skewed sample. The AI-generated work you notice is the work nobody put effort into. Well-refined AI content often goes unnoticed as AI-made. Readers’ suspicion isn’t unreasonable, but from the sample they see, it’s easy to conclude that AI only produces slop. You can’t see the review behind a piece, but you can see whether it holds up. Final quality is a good indicator of how much effort and review went into it, because work that’s accurate, focused, and well shaped rarely gets there without someone applying judgement.

We Don’t Blame Cameras

Almost everyone has access to a camera. However, most photos taken are blurry, badly framed, or just forgettable. Nobody concludes that the cameras are the problem because professional photographers use the same device to produce excellent work. These professionals decide what to shoot, which frame to keep, and what to adjust post-capture.

The camera makes taking a photo cheap, but it doesn’t decide whether the photo is any good.

AI works the same way. It makes producing text, code, and images cheap, but someone still has to decide whether the result is good enough to share. With AI, that decision starts with the person who ran the prompt.

What Does Good Judgement Look Like?

Expertise helps, but you can also build judgement through simple habits.

First, know what you want before you prompt. Decide who the output is for, what it should say, and what a good result will look like. If you don’t decide this upfront, the model will fill the gaps with generic content.

Second, use AI most where you already have the expertise to verify the results. You’ll catch bloated or wrong output in areas you know well. In less familiar areas, learn enough to check it, slow down, and check against other sources.

Then, read everything before you share it. Read it the way your audience will. Review your code before a teammate reviews it.

As you review it, cut whatever AI over-produces. Remove any “fluff” that was not part of the initial prompt.

If there are factual claims in the output, verify all of them. Check names, numbers, quotes, and links.

Finally, decide whether you would proudly put your name on the final output. If you wouldn’t, it’s probably not worth sharing.

Conclusion

Slop isn’t a tool problem. Humans that choose to publish it are. Abandoning AI because of slop treats the symptom and throws away a powerful tool that helps people produce good work quickly when used with care.

If you create with AI, treat the output as a first draft and decide for yourself whether it’s good enough before you share it. Is it accurate? Could it do the same job in fewer words? Iteratively refine the draft until it’s ready to share.

If you’re scrolling social media and are on the receiving end of slop, you have a part to play too. Don’t comment “AI Slop!!” on the post. A feed treats engagement as a signal to show more people, so even posting a mocking comment helps that content spread. When you come across slop, give it no attention instead.

Support AI-generated work that shows evidence of review and refinement. Share it and link to it. If readers consistently reward good work and ignore bad work, algorithms will highlight good uses of AI and filter out the sloppy ones, and creators will have a reason to care about quality.

But that only works if we judge work by its final quality, and not by whether AI was involved.