Google’s Content Effort Signal Isn’t a Writing Problem

When a trove of Google’s internal documentation leaked, the SEO world spent weeks combing through it. One attribute stood out. It was called contentEffort, and it was described as an effort estimate for article pages, generated by a large language model. It sits among page-level quality signals that can feed into preliminary scoring.

Google hasn’t confirmed it’s a ranking factor. It hasn’t published a scoring model or a target score (they rarely do). But the idea behind it isn’t mysterious. The signal appears to measure how hard a page is to create. More specifically, it measures how easily a page could be replicated. Generic, templated content that an AI tool could produce in 30 seconds likely scores low. And Google’s rater guidelines already ask raters to consider how much a person actively worked to create satisfying content.

If that sounds familiar, it should. Google has been giving the same advice for more than a decade: make good content, and the metrics will follow.

What’s changed is the math. When every company can generate a high volume of passable articles, passable stops being a strategy. So why is your content worth reading over someone else’s?

What the problem is (and isn’t)

You don’t need a trained eye to recognize good writing. Anyone can feel it when voice, flow, and knowledge come together. Anyone can feel it when they don’t.

Good writing matters. But writing is a delivery system for an idea, and it can only carry what it’s given. What good is it to beautifully write nothing special?

That’s a common AI problem. A model will produce clean sentences all day. Whether it produces an original business perspective depends on its inputs: proprietary context, current developments, and editorial direction. Most prompts carry none of the three. What comes out is a recycled take. Fluent, organized, and interchangeable with everything else on page one. Or, just as often, a bowl of jargon soup.

Padding a thin idea to 2,500 words or polishing it for another week doesn’t add effort. It adds evidence of its absence.

You’ve probably felt this when repurposing content. You cut last month’s blog into a LinkedIn post, a newsletter segment, and a sales one-pager, and each one lands a little softer than the last. A strong idea survives that trip. Repetition amplifies it. A weak one thins out with every pass. You’re spreading too little butter over too much bread.

What “high-effort” actually looks like

Google won’t publish a rubric. Based on our experience and reading, high-effort content tends to share five traits.

A timely, relevant angle. It responds to something happening now, in a way that matters to a specific audience.

A distinct point of view. It takes a position someone could disagree with. Quick test: could a competitor publish the same piece with their logo on it? Then keep digging.

Specific examples and implications. Named developments, concrete consequences, details a reader can check. They’re the hardest thing to fake and the easiest to spot when they’re missing.

Useful recommendations. The reader leaves knowing what to do next.

Presentation that respects the reader. Headings someone can scan, a table where a comparison helps, prose where an argument needs room. It’s effort a reader can see, and search results and AI-generated answers favor it.

A table built around a thin idea just makes the thinness easier to skim. The first four traits come from research and judgment. That’s where the bottleneck lives.

Picture your last brief. You know what good looks like. You also have fourteen tabs open, a competitor’s post half-read, three industry newsletters unopened, and a draft due Thursday. Finding the timely angle, the gap in the conversation, and the example worth citing takes hours of reading you’ll mostly never use. By the time the brief comes together, the deadline has already eaten the draft.

We aimed AI at the wrong part of the process

The first wave of AI content tools went straight for drafting, because drafting felt like the slow part. The slow part was always figuring out what to say.

Point AI at the draft and you get faster recycled takes. Point it upstream, at the research and ideation that happen before anyone writes a word, and the work changes. The question shifts from “What can we talk about?” to “What should we say about this?” With a full field of options in front of you, you can pick the one with enough depth to resonate.

The most important character in your content is your customer. The better you understand what matters to them, the better you can answer their questions. That goes for your website and for real life.

That’s the job Chatter was built for. It’s a content intelligence layer that sits upstream of your writing process. It doesn’t write your content. It finds, curates, and structures the raw material so your team, and whatever drafting tool you use, has something real to work with.

It builds an intelligent feed of the things your audience is reading and writing so you can jump into the conversation instead of shouting at them from the sidelines. 

Timely angles come from Stories. Chatter surfaces news and industry coverage filtered to your brand’s keywords, audience personas, and solutions. Every story links to the original, so every claim traces back to something that was actually published.

A distinct point of view starts with knowing the field. Competitor monitoring shows what other voices in your space are publishing and what’s being written about them. You can see the angle they took, and the one they missed.

Specificity comes from Topics. Select the stories worth building on, and Chatter generates a structured brief that merges that material with your brand context: audience, angle, and outline. Starting from an idea instead? The Wizard builds a structured topic from your idea, grounded in the same inputs.

Useful recommendations and presentation come from you. That’s the part no tool should do for you. Chatter hands your team a sharper starting point. Your expertise decides what to recommend and how to show it.

When the brief is ready, export it as markdown into your drafting workflow. A model working from source material and a defined audience has something concrete to react to. That’s how you break the AI Ouroboros.

Originality begins before the draft

Google’s Content Effort signal is asking one question: could this page have come from anywhere?

Quality comes from two things: the inputs you start with, and the editorial judgment you apply to them. The judgment is yours. Chatter keeps the inputs current, relevant, and traceable to their sources.

Google can tell when you didn’t try. So find something worth saying, then make the effort show.

Start for Free →