Stop AI Slop

AI slop is not defined by who—or what—wrote the first draft. It is defined by what the publisher failed to do afterward.

The familiar signs are easy to spot: an inflated introduction, a stack of obvious claims, smooth transitions between ideas that do not quite connect, and a conclusion that repeats the introduction in slightly different words. Nothing is clearly false, but nothing feels necessary. The text occupies space without earning attention.

This problem existed before generative AI. Companies published empty thought leadership. Search-driven websites stretched one useful paragraph into two thousand words. Teams filled documents with language designed to survive a meeting rather than support a decision. AI did not invent low-accountability writing. It made that writing nearly free to produce.

The result is a volume problem, but the solution is not merely to write less. The solution is to make every part simpler, clearer, and more deliberate.

Simple is not the same as short

Short writing can still be vague. Long writing can still be clear.

A useful article may need history, examples, objections, and technical detail. Removing those elements to reach an arbitrary word count does not improve the work. It only makes the work incomplete. Simplicity comes from structure: the reader can see what matters, why it matters, and how each section supports the main idea.

Good writing reduces the reader’s uncertainty. It does not reduce the subject until nothing meaningful remains.

That distinction matters when using AI. A model can compress a paragraph, but compression is not judgment. It cannot decide which detail your reader needs unless you have already made that decision. It cannot know which qualification protects the argument from being misleading. It cannot tell whether a clean sentence hides a weak idea.

The writer remains responsible for those choices.

Begin with a job, not a topic

“Write about software quality” is a topic. It gives a model room to produce every sentence the internet has already seen.

“Explain why our release process allows configuration errors to reach production, then recommend one change the team can test this month” is a job. It has a reader, a problem, and a decision.

Before drafting, write down three things:

1. Who needs to read this?
2. What should they understand when they finish?
3. What should they be able to do next?

If those answers are unclear, more prose will not help. Research the problem, talk to the people involved, inspect the evidence, or narrow the scope. Do not ask a model to cover uncertainty with confident language.

Make claims that can carry weight

Slop relies on claims that sound reasonable because they are too broad to challenge:

In today’s rapidly changing landscape, organizations must embrace innovation to stay competitive.

The sentence is polished and useless. Which organizations? What changed? What kind of innovation? Competitive by which measure? What happens if they do nothing?

A stronger sentence accepts the risk of being specific:

Our weekly release cycle now spends more engineer time on manual verification than on deployment. Automating the three repeated checks would remove the current bottleneck without changing the approval process.

The second version can be tested. A reader can disagree with it, ask for the numbers, or act on it. That is a feature. Professional writing is not language that avoids criticism; it is language that makes useful criticism possible.

Prefer concrete nouns and active verbs. Name the system, team, behavior, cost, or constraint. Replace “improve efficiency” with the actual change. Replace “stakeholders” with the people who must decide. Replace “best practices” with the practice and the conditions under which it works.

Specific language is often simpler because it removes the need for decoration.

Use AI for transformation, not authority

AI is valuable when the source material is real and the requested operation is clear. It can organize notes, compare two drafts, expose repetition, generate counterarguments, or rewrite a dense passage for a different audience. These tasks transform material you can inspect.

Problems begin when the model becomes the source of authority. A fluent answer can hide missing evidence, collapsed distinctions, or invented detail. The surface quality makes weak reasoning harder to notice.

A safer workflow separates thinking from generation:

1. Gather the facts, examples, and constraints yourself.
2. Decide the central claim and the order of the argument.
3. Use AI for bounded drafting or revision tasks.
4. Verify every factual claim against the original source.
5. Rewrite the final text until it reflects your own judgment.

Do not publish a sentence simply because it arrived fully formed. If you cannot explain why it is present, remove it.

Edit in separate passes

Trying to fix truth, structure, tone, and punctuation at the same time is inefficient. Each problem requires a different kind of attention.

Start with a truth pass. Check names, dates, quotations, causal claims, and any statement that depends on outside evidence. Confirm that certainty in the prose matches certainty in the facts.

Next, make a structure pass. Give each section one purpose. Move the strongest evidence close to the claim it supports. If two sections make the same point, combine them. If a section does not change the reader’s understanding, delete it.

Then make a sentence pass. Replace abstract phrases with direct language. Cut unnecessary openings such as “it is important to note.” Remove transitions that explain a connection the reader can already see. Vary sentence length, but do not mistake rhythm for substance.

Finish with a deletion pass. Look for sentences that summarize the sentence before them, paragraphs that announce what the next paragraph will say, and conclusions that merely restate the title. Delete them without asking the model to fill the empty space.

The goal is not minimalism. The goal is density: more meaning per unit of attention.

Put a name behind the work

The easiest way to produce slop is to treat publication as an automated final step. Generation finishes, the pipeline runs, and text appears under a company or product name. No individual has to say, “I believe this is accurate, useful, and ready.”

Restore that moment of responsibility.

Every published piece should have an owner. That person does not need to type every word, but they should be able to defend every claim. They should know the sources, understand the tradeoffs, and accept corrections. Ownership changes the standard from “the output looks complete” to “I am willing to put my name on this.”

That standard scales better than any detector. Detection asks whether AI was involved. Ownership asks whether a human exercised judgment. Only the second question measures quality.

Publish something worth reading

AI lowers the cost of producing sentences. It does not lower the cost of knowing what should be said.

The scarce parts of good writing remain the same: direct experience, careful research, a defensible point of view, and respect for the reader’s time. Use AI where it helps with the mechanical work. Do not let mechanical ease replace editorial responsibility.

Before publishing, ask one final question: if this article disappeared, would the reader lose anything specific?

If the answer is no, the draft is not finished.