Every content team has been told to publish higher-quality content, and almost none of them have been told what quality means in a way that survives contact with a spreadsheet. Content effort is the most useful attempt at that definition anyone has published this year, mostly because it refuses the obvious version.
The obvious version says effort equals work: hours spent, words written, revisions logged. Shepard's framing on Zyppy Signal is sharper than that. What gets evaluated is evidence of distinctive value, meaning the traces a page carries of containing something that could not have been assembled from other pages on the same topic. A team can burn eighty hours producing a page with no such trace, and a specialist can produce one in ninety minutes.
What content effort actually means
The distinction matters because it changes what you optimize. If effort is labor, the response is more resources. If effort is evidence, the response is different inputs: original measurement, first-hand access, proprietary data, primary sources, named expertise, artifacts the writer had to obtain rather than paraphrase.
Think about what a retrieval system can actually observe. It cannot see your calendar. It can see whether a page contains a number that appears nowhere else, a photograph that is not stock, a method section, a named practitioner with a track record, a dataset, a transcript, a specification. Those are the observable proxies for effort, and they are the only ones available to a machine.
| INPUT | COSTS A LOT OF HOURS | LEAVES OBSERVABLE EVIDENCE | VERDICT |
|---|---|---|---|
| Rewriting the top five results in your own words | Yes | No | Expensive and invisible |
| Running a small original test and publishing the method | Sometimes | Yes | The highest-return content investment available |
| Adding a comparison table built from vendor documentation | No | Yes | Cheap, underused, and highly extractable |
| Interviewing one practitioner and quoting them by name | No | Yes | Fast route to distinctive value |
| Expanding a 900-word post to 2,400 words | Yes | No | The most common wasted content investment |
| Publishing the failure case alongside the success case | No | Yes | Rare enough to be a differentiator on its own |
Look at the last column. The two rows that consume the most hours produce the least evidence, and they describe how most enterprise content programs spend their budget. That mismatch is the entire finding.
The evidence that effort, not authorship, is what gets scored
The strongest supporting data came three weeks earlier. Ryan Law and Xibeijia Guan published an Ahrefs study on July 27, 2026 covering 331,000 pages, and the results cut directly against the industry's favorite anxiety. Pages that were entirely AI-generated made up 5.3% of top-three rankings. Around 9% were at least 80% AI content. Pages under 50% AI content accounted for 82.2% of top-three rankings.
Share of top-three ranking pages by AI content proportion, from the Ahrefs study of 331,000 pages published July 27, 2026.
The same study found low and moderate AI content earning roughly two to three times the impressions of high and very high AI content. The authors' conclusion was that Google punishes low quality rather than AI authorship, which is the position we took when we covered why Google never punished AI content as such. Shepard's framework explains the mechanism: heavily generated pages tend to contain nothing that was not already on the web, so they carry no evidence of distinctive value, and the correlation with AI authorship is incidental.
How to run a content effort audit
The audit is mechanical once you accept the definition. You are scoring each page for observable evidence, not judging its writing. Five passes, in order, on your top hundred pages by business value rather than by traffic.
The triage step is where teams flinch. Pile three is usually larger than anyone expects, and consolidating it feels like destroying assets. It is not. A page that contains nothing distinctive competes against every other page that contains nothing distinctive, which is a competition decided by domain authority alone, and you will lose it to a larger site forever.
What the audit finds on most enterprise sites
Three patterns show up almost every time we run this. The first is that the pages with the most evidence are usually the least optimized: engineering write-ups, support documentation, and internal explainers that nobody in marketing has touched. They contain real specifications and real screenshots and they are structured badly.
The second is that the marketing pages with the most invested hours contain the least evidence. A pillar page assembled from competitor research consumed a quarter of somebody's year and contains nothing that was not already indexed. The third is that the fix is almost always additive rather than editorial: the page does not need rewriting, it needs one input the writer did not have.
The catalog and ecommerce version of this is sharper still, because product data is evidence by definition and most of it never reaches a public page in a readable form. That was the core of our print ecommerce engagement: the distinctive material already existed inside the business, and the work was exposing it rather than writing anything new.
The fourth pattern is about ownership rather than content, and it is the one that decides whether any of this survives a reorganization. Evidence-bearing inputs live with people who are not writers. The measurement lives with analytics, the specification lives with product, the screenshot lives with support, the named practitioner lives in delivery. A content team with no standing claim on those people can only produce the kind of page that scores zero, no matter how good its writers are. Fixing the audit findings is usually an access problem before it is a craft problem.
Where content effort stops paying
Take the position seriously enough to state its limits. Evidence of distinctive value does not rescue a page targeting a query with no commercial relevance to you, and it does not overcome a genuinely broken technical foundation. If a page cannot be crawled or rendered, its evidence is invisible and its score is irrelevant.
It also has a ceiling in categories where the answer is genuinely commodity. There is no distinctive way to state a standard conversion formula, and no amount of original photography makes one page about it better than another. In those categories the right move is to stop competing on the informational query and compete on the decision query instead, where comparison and verdict still differentiate.
And there is a timing caveat. Evidence-heavy pages take longer to earn their position because they take longer to accumulate corroboration. Teams in fast-moving categories like tech and SaaS often need a parallel track of quicker comparison content while the substantive assets mature. Running only one of the two is the most common planning error we see.
The work order
Run the five-day audit on your top hundred pages by pipeline contribution. Expect pile three to be a third of them. Then pick the ten pages in pile two with the highest business value and name the one input each is missing: a measurement, a named source, an original image, a specification. Assign those ten inputs as tasks, not as articles.
Do that for a quarter before commissioning anything new. Most content teams discover their library is not too small, it is too thin, and thinness is cheaper to fix than volume is to create. The scoring method is worth reading in full at Zyppy Signal's August 13 piece, and the deeper structural version of this argument is in our guide to content that earns AI citations.
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Tyler leads work at the intersection of SEO and generative engines at Something Inc., helping B2B brands get ranked and cited across every major AI engine.