Is AI Content Bad for SEO?

Let me save you a few minutes with a quick TL;DR.

No, AI content is not bad for SEO.

Great! Post over, job done. Ok on a serious note, in the rest of this article we will take a look at exactly why this is and try to dispel some of the fear that Google will suddenly penalise all AI content.

What is AI slop?

"Slop" was named word of the year by Merriam-Webster in 2025. It refers to low-quality, AI-generated content flooding the internet. Shrimp Jesus memes on Facebook. Fake historical photos. AI-written books clogging up Amazon. The narrative is clear: AI is ruining the web.

But low quality content aka "slop" isn't new. The tool changed. The problem is the same as always.

We've been here before

Before ChatGPT, content farms dominated search results. Writers churned out thin articles stuffed with keywords. Companies paid freelancers five dollars to rewrite the top Google result in slightly different words. Google's Panda update in 2011 wiped out thousands of sites built on that exact model: quantity over quality, gaming the algorithm instead of helping users. Demand Media was a prominent content farm valued at $1 billion in January 2011. They reported a loss of $6.4 million the year after Google implemented Panda.

Google's March 2024 core update targeted "scaled content abuse." Same problem, different decade. Only the production method changed.

When people complain about AI slop, they usually mean low-effort content produced at scale. That isn't an AI problem. It's a motivation problem. AI just made it faster.

The stats don't support the panic

The data tells a different story.

Ahrefs analysed 900,000 newly created web pages in April 2025 and found that 74% contained AI-generated content. But only 2.5% qualified as "pure AI" with no human editing.

A separate Ahrefs study of 600,000 ranking pages tells a similar story: 86.5% of content in Google's top 20 results includes at least some AI. Google doesn't punish AI. It ranks it.

No meaningful correlation exists between how much AI someone uses and where content ranks. The correlation was 0.011—effectively zero. Usefulness drives rankings, not the identity of the writer or the tool.

A few more data points worth noting: 87% of marketers now use AI to help create content. AI content is 4.7x cheaper than human-written content ($131 per post vs $611). And here's a counterintuitive one: human content was 4% more likely to be negatively impacted by a Google update than AI content.

The CGI parallel

I have friends in the AI video space. They hear the same criticism: "It's such a shame we're using AI for video now."

But people said the same thing about CGI. Critics said it would kill practical effects, ruin the craft, and make everything look fake. Now CGI is standard. The craft didn't disappear. It evolved.

AI content is on the same trajectory. The people producing genuinely impressive AI videos don't type one prompt and hit generate. They spend hours on inputs: the imagery, the style, the iterations, the feedback loops. The process isn't magic. It's work, applied differently.

The real differentiator: input, not output

Here's what most people miss about AI content.

Output quality is converging. Whether you use Claude, ChatGPT, Gemini, or Grok, they all write better with every iteration. The models are constantly improving. They understand context better. The prose gets cleaner. Soon, all of them will produce content that feels "good enough" by default.

So the real question isn't which AI you use. It's what you feed it.

The gap between slop and quality content isn't human vs. AI. It's whether someone invested in context, research, structure, and references. Type "write me a blog post about SEO" into any model and you'll get something generic. Give the same model a detailed brief, competitor analysis, specific examples, and a clear structure, and it will produce something far better.

AI can't create your insight on its own. It can synthesise information, but pattern recognition from experience, a point of view shaped by having done the work, knowing what matters because you've seen what doesn't—that still comes from you. The quality of the input directly determines the quality of the output.

Google isn't going to punish AI content

This matters more than most people realise: Google isn't going to broadly penalise AI content. Fear of AI detection misses the point.

First, detection is a moving target. As AI models improve, reliable detection gets harder. The chase never ends.

More importantly:

Detection doesn't serve Google's business. Google succeeds by surfacing useful content. If results get worse, users leave. Google's entire value proposition pushes them to reward quality, not to police which tool created the content.

Google has said this directly. Their Search Central documentation states: "Generative AI can be particularly useful when researching a topic, and to add structure to original content. However, using generative AI tools or other similar tools to generate many pages without adding value for users may violate Google's spam policy on scaled content abuse."

They've also drawn the same historical parallel in their February 2023 guidance:

"About 10 years ago, there were understandable concerns about a rise in mass-produced yet human-generated content. No one would have thought it reasonable for us to declare a ban on all human-generated content in response. Instead, it made more sense to improve our systems to reward quality content, as we did."

Google cares about quality and usefulness, not origin. If your article answers a query better than a competitor's article, presents information clearly, and provides genuine value, it will rank. AI involvement doesn't change that.

In June 2025, Google issued manual actions to sites for "scaled content abuse." But look at what triggered the penalty: not AI involvement, but mass-producing pages without adding value. The method didn't matter. The quality did.

If your content is slop, if it adds nothing new, just summarizes other sites, and contributes no value: it won't rank. But that outcome doesn't happen because AI wrote it. It happens because the content is bad. The same fate awaits human-written slop.

The humaniser trap

This brings me to humanisers, tools that claim to make AI content "undetectable."

If you rely on a humaniser, you're solving the wrong problem.

Ask yourself who wins that arms race: A small startup trying to trick detection, or Google with essentially unlimited resources? If Google wanted perfect AI detection, they would get there first because they simply have more resources to do it.

Even beyond that, humanisers often make content much worse. They introduce odd phrasing and intentional errors to confuse detectors. The resulting content might pass an AI detector, but it becomes much harder to read. You "win" at detection but lose at the only thing that matters: being useful to humans.

Worrying about detection usually signals a deeper issue. It suggests you're not confident in the quality of your content. You're likely using AI to avoid work, not to do better work faster.

A different approach

I'm writing for Machined, an AI content tool. You might wonder why I'd write an article saying AI can produce slop. Wouldn't that go against our business?

Actually, it's the opposite.

Every AI tool can write. The difference is what happens before the writing starts.

With a raw LLM, you're responsible for everything: the prompts, the structure, the voice, the keyword strategy, the clustering. Most people skip that work because it's tedious, and they end up with generic output.

Machined systematises that layer. The prompts are already extensive. The keyword clustering is built in. The voice frameworks exist. You're not starting from "write me a blog post". You're starting from a foundation that would take hours to build yourself.

But systematised inputs aren't the same as your inputs. What we can't automate is your angle, your examples, your experience, your knowledge of what your audience actually needs. That's still on you. The tool gives you a higher floor. What you build on it determines the ceiling.

We're not building a product to trick Google or automate thinking. We're building tools that handle the commodity work so you can focus on the work that actually differentiates.

The tool isn't the problem

Slop existed before AI. People just produced it more slowly.

AI didn't create the incentive to publish low-quality content at scale. That incentive already existed. AI just removed the friction.

But just as AI can produce slop faster than ever, it can also help you produce high-quality content just as quickly. Research that once took hours now takes minutes. Outlines that require deep expertise can be scaffolded and refined. Drafts that once took days can evolve in real-time.

The tool isn't good or bad. It multiplies whatever you bring to it.

Lazy inputs produce slop. Thoughtful inputs produce something worth reading.

The real question isn't whether you use AI. It's whether you're willing to bring something worth multiplying.