The Content Effort Attribute from Google's Content Warehouse Leak

When the Google Content Warehouse API leak first surfaced in May 2024, the industry was immediately thrown into a spin. For me, however, the real signal came later.

I had been obsessively tracking the U.S. Department of Justice's (DOJ) antitrust trial against Google, and it wasn't until trial testimony in early 2025 all but confirmed the legitimacy of the leaked data, specifically around systems like NavBoost, that it became 'game on'.

It felt like decades of speculation were being replaced by a canon of truth, right in the public record.

This convergence of the leak and the trial testimony was an opportunity I couldn't pass up: to finally build an evidence-based SEO framework based on Google's own blueprints.

This special analysis for Searchable.com is the result of that deep investigation.

The contentEffort Attribute as the Technical Lynchpin of Google's Quality Systems

In May 2024, a "bombshell" document leak provided an "unprecedented look under the hood" of Google's search systems.

Originating from an accidental publication to Google's own GitHub repository, the leak exposed over 2,500 pages of internal Content Warehouse API documentation. This documentation detailed 14,014 specific attributes, or features, that Google collects and stores to process, rank, and retrieve web content.

While the leak revealed thousands of data points, the central focus of this analysis is the contentEffort attribute.

Based on my extensive analysis of the leak's documentation, this attribute is defined as an "LLM-based effort estimation for article pages". This single attribute is arguably the most strategically significant revelation, as I have identified it as the potential technical engine driving Google's "Helpful Content System" (HCS).

I describe the function of contentEffort as a "direct countermeasure to the growth of low-effort, scaled, and AI-generated content that lacks originality and depth".

This attribute signifies a fundamental paradigm shift in Google's approach to content evaluation. Historically, Google relied on abstract guidelines (such as E-E-A-T: Experience, Expertise, Authoritativeness, and Trust) and indirect proxies for quality (such as backlinks).

The contentEffort attribute signals a move toward the direct, algorithmic quantification of human labor and resource investment in the content creation process.

This system is not merely detecting AI-generated text; it is programmatically scoring the absence of human effort.

The attribute's function appears to be what I've assessed as the "'ease with which a page could be replicated'".

By algorithmically identifying and scoring content based on this principle, Google is attempting to programmatically disincentivize the economic model of "content farms" and low-cost AI generation, fundamentally altering the return on investment for scaled, low-effort content production.

Foundational Analysis: The Primary Sources Defining contentEffort

In my view, understanding the contentEffort attribute starts with identifying the primary sources who brought the leak to light and analyzed it. The chronology of the leak is critical to establishing this foundation.

The Leak Origin and Coordinated Release

The leak's timeline begins months before its public explosion:

March 13/27, 2024: An automated bot, yoshi-code-bot, inadvertently publishes internal Google Content Warehouse API documentation to a public GitHub repository.

May 5, 2024: SEO professional Erfan Azimi discovers the public repository and shares the documentation with Rand Fishkin of SparkToro.

May 7, 2024: Google, having become aware of the exposure, removes the public GitHub repository. However, the documentation had already been indexed and captured by external automated documentation services.

May 27, 2024: Following weeks of internal review and vetting the documents with ex-Google employees to confirm authenticity, Rand Fishkin and Michael King publish "dual reports". This coordinated release brings the leak to "global public attention" and "ignit[es] widespread industry discussion".

May 29, 2024: Google issues an official statement to the media, cautioning against "inaccurate assumptions" based on "out-of-context, outdated, or incomplete information".

The Primary Analysts: Rand Fishkin and Michael King

The individuals universally cited as the primary sources for the leak's analysis are Rand Fishkin and Michael King.

Rand Fishkin (SparkToro): Fishkin's foundational post, "An Anonymous Source Shared Thousands of Leaked Google Search API Documents with Me", framed the narrative. His analysis focused on the strategic implications of the leak, particularly its confirmation of long-debated SEO theories that Google's public-facing representatives had frequently denied. This included Google's use of clickstream data via the NavBoost system, the existence of a "sandbox" for new sites, and the clear contradictions with Google's public statements.

On a personal note, my history with Rand goes back decades. When he was leading SEOmoz (now Moz), he reached out and offered me his legal team to help fight an SEO-related legal case I was embroiled in. I eventually won, but that gesture from him during the 'wild west' days of SEO is something I haven't forgotten.

Michael King (iPullRank): King's post, "Secrets from the Algorithm: Google Search's Internal Engineering Documentation Has Leaked", provided the initial technical deconstruction. King, who is recognized for his deep technical expertise, cross-referenced the leaked API attributes with "DOJ antitrust testimony" and "extensive patent and whitepaper research". His analysis was the first to technically contextualize many of the specific quality-related attributes.

My Analysis: Defining the contentEffort Attribute

While Fishkin and King are the primary sources for the leak in general, my work provides the most exhaustive, granular, and focused analysis on the contentEffort attribute itself.

I think my analysis is the origin of the direct definition quoted from the documentation, identifying contentEffort as a "Large Language Model (LLM)-based effort estimation for article pages".

Furthermore, my own published analysis identifies the attribute's precise technical location: it is a variable within the broader pageQuality framework, located specifically in the QualityNsrPQData module.

The key functional interpretation I derived is that the attribute is used to assess the "'ease with which a page could be replicated'".

Content that is difficult and costly to reproduce, such as that which includes "original data, expert interviews, and custom visuals", signals a higher level of effort and, therefore, is intended to receive a higher score.

Further Discoveries: Goldmine and Firefly

In addition to deconstructing contentEffort, my deep investigation into the leak's modules uncovered other previously unknown and unexplored Google systems. I discovered and named the 'Goldmine' system, which appears to be a universal quality engine that evaluates elements for the search results page, such as your title tag, pitting it against other candidates like H1 tags and body text.

My analysis also identified the 'Firefly' system, documented as QualityCopiaFireflySiteSignal. I've concluded this system is a key enforcement mechanism for Google's "scaled content abuse" policy, designed to identify and measure patterns of low-quality, high-volume content production.

The contentEffort Ecosystem: Related Quality Attributes

My analyses reveal that contentEffort does not work in isolation. I've found it is part of a sophisticated, multi-layered "effort" assessment system. The granularity of this system is revealed by other, related attributes:

ugcDiscussionEffortScore: The leak details a separate score for User-Generated Content. This is defined as "A score for the quality/effort of user-generated content (comments/discussions)". Michael King's analysis highlights that this attribute is used to distinguish "low-effort discussion content," such as comments reading "Great post, thanks!," from high-effort contributions like a "long, detailed review on a product discussion forum".

OriginalContentScore: This attribute "suggests Google can identify and score true original content higher".

The existence of these three distinct attributes (contentEffort for articles, ugcDiscussionEffortScore for discussions, and OriginalContentScore for uniqueness) demonstrates a sophisticated disaggregation of "page quality." Google's engineering is not lumping quality into a single heuristic. This technical separation confirms that Google can distinguish between and independently value a page's main content and its supplementary content. A practical implication is that a website can build "quality" through either a high-effort article (high contentEffort) or a high-effort community discussion (high ugcDiscussionEffortScore), and Google possesses the specific, independent tools to measure both.

contentEffort as the Technical Implementation of HCS and E-E-A-T

My primary analyses of the leak converge on the conclusion that the contentEffort attribute is the "technical lynchpin" of Google's public-facing quality guidelines, specifically the Helpful Content System (HCS) and E-E-A-T.

The Engine of the Helpful Content System (HCS)

My hypothesis is that the contentEffort attribute is the HCS, or at least its primary technical mechanism. Google's HCS was introduced with abstract, subjective questions for creators, such as, "Does your content clearly demonstrate first-hand expertise and a depth of knowledge (for example, expertise that comes from having actually used a product or service, or visiting a place)?".

Frankly, whether one believes the leak is real or "out-of-context" is irrelevant to this specific point. The concept of contentEffort is a direct, 1:1 mapping to Google's own public "Helpful Content" guidelines.

These guidelines ask creators to self-assess with abstract questions that require a system like contentEffort to exist. For instance, Google asks: "Does the content provide original information, reporting, research, or analysis?" and "Does your content clearly demonstrate first-hand expertise and a depth of knowledge...?". Perhaps most tellingly, the guidelines penalize content that is just "simply copying or rewriting those sources" or "mainly summarizing what others have to say without adding much value". These questions are, in effect, a plain-English description of the contentEffort attribute. They are all asking the fundamental question that I believe the attribute is designed to score: 'How much non-replicable human effort went into this page?'.

The contentEffort attribute provides the "precise technical mechanism to answer these questions algorithmically and at the scale of the entire web". My analysis posits that Google is using its own Large Language Models (LLMs) to assess "depth of knowledge" and "original research" by analyzing factors such as linguistic complexity, the presence of unique data, and other signals of human labor.

I analyze it as the "core classifier for the HCS". A low contentEffort score would serve as a strong signal that the content is not "people-first," which could then trigger the "site-wide demotion that is characteristic of the HCS". This provides a direct engineering link between the attribute and the "site-shattering" core updates of 2024, which aggressively targeted "scaled" and low-effort content.

An Algorithmic Proxy for E-E-A-T

The leak also clarifies how Google algorithmically measures the abstract concepts of E-E-A-T.

My analysis confirms that "there is no single 'eeat_score'". Rather, E-E-A-T is what I've described as an "emergent property of dozens of granular attributes". The leaked attributes provide a "quantifiable, scalable" system for Google's engineering to measure the concepts defined in its human-rated Search Quality Rater Guidelines.

The attributes map to the concepts as follows in my analysis:

contentEffort maps directly to Expertise and Experience.

siteAuthority and homepagePagerankNs map to Authoritativeness.

scamness and unauthoritativeScore map to Trust.

siteFocusScore ("how dedicated a site is to a single topic") and siteRadius ("how far a specific page's topic deviates from the site's central theme") map to Topical Authority, a key component of Authoritativeness and Expertise.

The following table synthesizes this mapping, connecting Google's public framework to its internal engineering attributes.

Table 1: Mapping Google's Public E-E-A-T Framework to Leaked Technical Attributes

The Antitrust Trial Connection: Corroboration and Context

A key part of the research query concerns whether the contentEffort attribute was discussed in the U.S. Department of Justice (DOJ) v. Google trials.

No Direct Mention of contentEffort

It must be stated clearly that the available research does not indicate the contentEffort attribute was specifically named or discussed in testimony during either the DOJ's search monopoly trial or its ad tech monopoly trial.

The NavBoost System as the "Rosetta Stone"

The true relevance of the antitrust trial is not that it mentioned contentEffort, but that it served to corroborate and authenticate the credibility of the entire leaked dataset.

This corroboration centered on a system called NavBoost. The connection was established through three independent points of triangulation:

Trial Testimony: In the DOJ search monopoly case, Google's Vice President of Search, Pandu Nayak, testified under oath about the existence and function of the NavBoost system.

Leaked Documents: The 14,014 leaked attributes also contained multiple references to NavBoost, confirming it as a critical system that "analyzes user behavior" and "reranks results based on their click metrics".

Anonymous Source Claims: Rand Fishkin's original anonymous source also independently identified NavBoost as a key system that used clickstream data from the Chrome browser to influence rankings.

This convergence of three separate sources (public testimony, the leaked API, and the original source's private claims) acted as a "Rosetta Stone" for analysts. Because the public, under-oath testimony proved the leak was authentic and accurate regarding NavBoost (a system Google had long been opaque about), analysts could then confidently assert that the rest of the 14,014-attribute leak was also credible and highly likely to be accurate. The trial's role, therefore, was not to reveal contentEffort but to authenticate the dataset from which contentEffort was discovered.

Industry Reception and Chronology of Discourse (May 2024 to November 2025)

The reception of the leak and the reputation of its primary researchers, Rand Fishkin and Michael King, have evolved significantly from the initial publication to the present (November 2025).

The Initial "Bombshell" (May to June 2024)

This period was defined by the "dual reports" from Fishkin and King, which "ignited widespread industry discussion". The dominant narrative, as framed by the researchers, was the exposure of Google's public "lies", "misdirection," and "gaslighting".

The analysis focused on direct contradictions to Google's public statements. The leak was seen as confirmation that Google does use:

Clickstream Data: (via NavBoost and Chrome).

"Domain Authority": (A siteAuthority metric).

A "Sandbox": (The hostAge attribute "to sandbox fresh spam").

In this initial phase, Fishkin and King were widely framed as industry truth-tellers who had exposed Google's long-standing opacity.

The Rebuttal and Skepticism (Late 2024)

This narrative was quickly met with two counter-narratives.

Google's Official Rebuttal: Google's May 29 statement that the data was "out-of-context, outdated, or incomplete" became the official line. Google confirmed it would not clarify which attributes were used or how they were weighted, citing the need to prevent "manipulation".

Expert and Community Skepticism: This ambiguity fueled a skeptic camp. SEO expert Eli Schwartz was widely quoted, stating, "we have no indication that these elements are used, if they are, and how they are used". Others dismissed the data as "murky" and "not the full algorithm". A more aggressive counter-narrative also emerged, painting the researchers as "SEO spam companies" who were angry that "Google didn't tell them the exact right way to F*** up Google search results". This highlights the significant tension in how the researchers' work was perceived.

The "Intentional Leak" Theory (September 2024)

A more sophisticated meta-analysis emerged in late 2024, most notably from analyst Holly Miller Anderson, which proposed "there was no leak". This theory posits that the documents were intentionally and "purposefully shared with prominent industry SEO veterans," namely Fishkin and King.

Miller Anderson employed the "Chef's Ingredients" analogy to explain this: Google provided the ingredients (the 14,014 attributes) but not the recipe (the weightings, or "how it's all put together'). This missing 'recipe' is what I uncovered in my analysis of the DOJ trial, where testimony referred to these weightings as the 'curves and thresholds' that engineers fine-tune.

This theory reconciles the "bombshell" and "skeptic" camps.

It suggests Google's "out of context" statement was strategically true, not a dismissal.

I myself likened the leak to the 'orgy of evidence' scene in the film Minority Report, where Colin Farrell's character, Danny Witwer, analyzes the photographs on the bed and realizes the setup. In this interpretation, Google (facing DOJ trials and a search quality crisis from AI spam) weaponized the leak.

By "leaking" the technical proof (e.g., contentEffort, siteAuthority) to the industry's most credible researchers (Fishkin, King), Google effectively forced the entire SEO industry to realign around "white hat" principles.

It provided the technical evidence that Google's engineering rewards effort and authority, making it the only viable path forward.

Vindication and 2025 Consensus

As of November 2025, the SEO industry is described as "turbulent", defined by the mass adoption of "AI Overviews" and a "major indexing crisis". In this chaotic environment, the 2024 leak is now viewed as the "treasure map" that proves "white hat SEO has purpose in 2025". The principles the researchers championed (focusing on user-centric content, authority, and effort) are now the broad consensus for a stable, long-term strategy.

The researchers' reputations have been solidified. Michael King wrote a post urging the community to "Send Rand Fishkin an Apology" regarding the long-running NavBoost debate, calling Fishkin's work "thankless".

Fishkin and King continue to be the primary advocates on the leak, hosting joint webinars. King's status was further cemented by being named "2025 Search Marketer of the Year" by Search Engine Land, a title well deserved.

In summary, the reception of the researchers (Fishkin, King, others like Cyrus Shephard, whom first brought to my attention the actual leak, and myself, by extension) transitioned from "whistleblower" (May 2024) to "targets of skepticism" (Late 2024) to, ultimately, the "established authorities" (2025) who provided the foundational map for the new era of search. I think.

Conclusion: Strategic Implications of contentEffort in the 2025 Search Landscape

The contentEffort attribute is not merely a historical artifact from a 2024 leak; it is the key to understanding the strategic landscape of 2025. This landscape is defined by two dominant, interrelated crises for content creators:

AI Overviews: The rollout of AI-generated summaries at the top of search results is commoditizing simple information. This has been shown to reduce user clicks to the underlying websites.

The Indexing Crisis: A "major indexing crisis" beginning in May-June 2025 has seen Google "systematically remove millions of pages from search results". This is seen as an acceleration of the "March 2024 AI content crackdown".

The contentEffort attribute is the strategic solution that explains both phenomena. The leak and its analysis validated that the only sustainable strategy remaining is a "focus on quality content and user experience".

In a search environment where AI Overviews are commoditizing information (the "what"), the contentEffort attribute reveals (as I have detailed) that Google's algorithm is pivoting to reward the only thing AI cannot easily replicate: demonstrable, high-cost, human-centric effort (the "how").

I speculate that the mass de-indexing of 2025 is the enforcement of this new standard. Pages that are algorithmically determined to be low-effort (possessing a low contentEffort score) are, in my view, being purged from the index because they are now redundant in an AI-first search environment.

My final assessment is that the contentEffort attribute is the most significant strategic revelation from the 2024 leak.

As I've concluded, it provides the "precise technical mechanism" for Google's economic war on low-effort content. It algorithmically mandates that creators invest in non-replicable, high-labor, human-centric assets (what I term "original data, expert interviews, and custom visuals") as the only viable path to survive and thrive in the 2025 search landscape.

The primary sources (Fishkin, King, others since, and now, myself) did not just expose a ranking factor; we revealed the new economic model for the future of digital content.

The content effort score is just one of the signals from the Google leak I've been asked to help develop at Searchable.com.

Stay tuned!

Referenced Sources

The Google Content Warehouse Leak 2024: https://www.hobo-web.co.uk/the-google-content-warehouse-leak-2024/

The 'contentEffort' Attribute, The 'Helpful Content System' and 'E-E-A-T': Is Gemini Behind The HCU?: https://www.hobo-web.co.uk/the-contenteffort-attribute-the-helpful-content-system-and-e-e-a-t-is-gemini-behind-the-hcu/

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Google users are less likely to click on links when an AI summary appears in the results: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/

In 2025, the only SEO strategy that remains effective is a focus on quality content and user...: https://medium.com/@passivemoves/in-2025-the-only-seo-strategy-that-remains-effective-is-a-focus-on-quality-content-and-user-9376ec797d27

About the Author: Shaun Anderson (AKA Hobo Web) is a primary source investigator of the Google Content Warehouse API Leak with over 25 years of experience in website development and SEO (search engine optimisation).

AI Usage Disclosure: Shaun uses generative AI when specifically writing about his own experiences, ideas, stories, concepts, tools, tool documentation or research. His tool of choice for this process is Google Gemini Pro 2.5. All content was conceived, edited, and verified as correct by Shaun (and is under constant development). See the Searchable AI policy.