AI Slop

AI slop is an informal, critical label for low-effort content that is mass-produced with generative AI tools and published with little or no human review, originality or care. It covers text, images and video: thin articles assembled by a chatbot, generic illustrations, auto-generated videos built on recycled scripts. The label describes how the tools are used, so the same model can help produce useful work or slop depending on the process around it.

How it works

Slop usually comes from the same pattern: a prompt template is run over a long list of keywords, products or places, and the output goes live without anyone checking it. The results tend to share a few traits:

• They repeat what already ranks and add no first-hand experience, data or point of view, so they offer no information gain.
• They contain unchecked errors, because language models predict likely wording and can hallucinate facts.
• They are produced at a volume no team could review, often across hundreds of near-identical pages.

Google’s position is that it judges the quality of content, whatever produced it. Its 2023 guidance says appropriate use of AI is not against its guidelines, while using AI to generate content primarily to manipulate rankings is spam. In March 2024 Google added a scaled content abuse spam policy: many pages generated for the primary purpose of manipulating rankings and not helping users, no matter how they are created. The first example it lists is using generative AI tools to generate many pages without adding value for users. Its guidance on generative AI content also asks site owners to fact-check and review all AI output before publishing, including titles, meta descriptions, structured data and image alt text. Content that fails Google’s helpful, people-first content questions and its E-E-A-T signals is unlikely to do well, AI or not.

How it’s used in SEO and marketing

For marketers the useful question is how to get the speed of AI without producing slop. MLforSEO’s approach rests on a few habits:

• Use automation for research, ideation and repurposing. The content transformation lesson in our Introduction to ML course encourages turning webinars and transcripts into new formats, and draws the line at spamming auto-transcribed posts, replacing the content team or cloning competitors’ videos.
• Build in human review for accuracy, safety and brand voice before anything is published.
• Publish original data, experiments and first-hand examples, which a model cannot copy from the pages it was trained on.
• Cover entities and their attributes specifically and accurately, so each page says something concrete.

Example

A travel site runs one template through an LLM for every town in a country: “Top things to do in [town]”. Most pages say the same vague things and some invent attractions. That fits Google’s description of scaled content abuse. A better version publishes pages only for places the team has data on, fills the template with first-party details such as opening times and prices they collected, and has an editor check each page. AI still saves drafting time, and the page carries real information. The same logic applies to programmatic SEO in general.

Limitations and things to watch

• “Slop” is a judgement, with no formal definition. Use Google’s policy wording when you assess risk on a site.
• Whether AI was involved is the wrong test. Google’s policy applies to human-made scaled content too, and carefully reviewed AI-assisted content can be helpful.
• Disclose where it makes sense. Google suggests explaining how automation was used, and Merchant Center requires AI-generated product images to carry IPTC metadata.

Learn it on MLforSEO

• Introduction to Machine Learning for SEO, including the content transformation module and its quality checklist
• What Is Information Gain in SEO? How Google Measures Content Uniqueness
• Image SEO in 2026, including AI-generated image disclosure
• 10 Ways to Automate SERP Analysis with AI, with guard-rails for AI workflows