What is non-commodity content strategy - and why your rankings depend on it now
Most content programmes have a brief problem, not a writing problem. The brief produces substitutable output, the writing polishes it, and the result is a piece any competitor could have published from the same starting point. Google has been building systems to identify exactly that for years - and at Google Search Central Toronto in April 2026, Danny Sullivan gave the pattern a name.
The benchmark Google isn't shouting about yet
Danny Sullivan recently updated us on what Google rewards: content that is genuinely non-replicable versus content built from what anyone could find on a SERP. Google's team is moving away from the 'helpful content' language that dominated the last two years of SEO conversation and replacing it with something more structurally precise.
John Mueller made the same point in May 2025: 'Focus on making unique, non-commodity content that visitors from Search and your own readers will find helpful and satisfying.' It is a structural, precise test of what passes in a search environment where AI Overviews answer generic queries in seconds.
Killing the ambiguity is the point - helpful to whom, in what way, compared to what? Non-commodity content answers all three. If a competitor could publish your piece from the same brief, it's commodity. That's the test.
What non-commodity content means
Sullivan broke it into components at Toronto. Non-commodity content is unique - it brings a viewpoint or information that others lack or cannot easily replicate. It is specific - it addresses a particular instance, situation, or thing rather than general rules or generic steps. It is authentic - it demonstrates first-hand knowledge or experience, meaning the author was genuinely present for what they are describing. And it has depth that is structurally hard to replicate.
The real estate example circulating after the conference nails it: 'We offered $15,000 under list but waived the sewer scope because I personally crawled the line and saw it was PVC, not concrete.' That sentence is impossible to write without direct experience. It cannot be produced from a search query, a secondary source, or a well-constructed AI prompt. That specificity is the point.
The same principle applied to data: 'The average cost of commercial cleaning in 2026 based on 500 local quotes' is non-commodity. A generic overview of commercial cleaning pricing is not.
Commodity vs non-commodity: a direct comparison
Commodity content is defined by replaceability, regardless of quality. A piece can be well-researched, properly structured, and factually sound - and still be entirely substitutable. If a competitor with the same brief and a capable writer could publish something functionally identical, the content is commodity regardless of how well it is written.
Generic how-tos, definitional explainers, and category overviews are the most common examples. A well-prompted AI could generate '7 tips for first-time homebuyers' in under a minute. That does not make the tips wrong. It makes the piece interchangeable - and interchangeable content carries high churn risk. Algorithm shifts and AI Overview expansion all hit substitutable content first, because the same query can be satisfied from multiple sources simultaneously.
Commodity content still plays a supporting role. Foundational explainers give context, serve early-stage readers, and support internal architecture. It contributes scaffolding to a content programme - the structural layer that holds things together while the non-commodity work builds authority. A library built on commodity content loses authority and gets displaced first.
Why AI-generated content is commodity by default
Volume-first AI content strategies produce commodity content by design. The model draws on existing published information, recombines it competently, and delivers output that reflects the consensus view. AI output reflects consensus - competently recombined, but drawn from what already exists.
This is where the discomfort sits for content teams who have scaled AI production significantly. Substitutable content is exactly what Google's current systems are built to deprioritise in favour of content that carries the fingerprint of someone who was genuinely present for what they are describing.
AI tools add real value when the strategy is built correctly. They handle research synthesis and structural drafting efficiently. The constraint is that they cannot supply the first-hand knowledge the strategy depends on. That part has to come from a human who ran the campaign, tested the product, or dealt with the outcome. AI tools add value when shaping knowledge a human already holds.
The four traits that make content non-commodity
Sullivan's framework covers two paired dimensions: unique and specific on one axis, authentic and structurally deep on the other. Original data is the clearest version of structural depth. If your team surveyed 500 customers, ran a controlled test, or tracked a metric over 18 months, that data exists nowhere else. A competitor cannot publish the same finding because they don't have the same inputs.
First-person depth is the other underused differentiator. The author perspective that comes from genuinely running a process - the decisions made, the things that failed, the specific conditions that changed the outcome - is non-commodity by nature. It is also what the Experience dimension of Google's E-E-A-T framework measures.
E-E-A-T is the mechanism for non-commodity assessment
Google's E-E-A-T framework - Experience, Expertise, Authoritativeness, Trustworthiness - is how the non-commodity standard gets applied in practice. Experience is the dimension commodity content is not designed to address - it requires the author to have been present. Secondary research and synthesis do not satisfy it.
Making content non-commodity is a structural decision about what knowledge gets captured and how it gets published. Author bylines with real credentials and cited original data are the signals Google's systems use to distinguish content that carries the fingerprint of genuine presence.
How to build a non-commodity content strategy at the programme level
Non-commodity content is often treated as a piece-level problem, but the real power sits upstream. Without systems to surface internal knowledge, the brief defaults to what a SERP can supply.
I've seen this play out repeatedly when auditing content programmes - teams with genuinely differentiated expertise publishing the same generic frameworks as everyone else, because no one built the intake process to capture what the team actually knows. The strategy fix comes before the brief, and it also comes before the tool. That means creating structured processes for knowledge capture: regular input sessions with subject-matter experts and data collection workflows that produce proprietary findings. Without those systems, the brief will always default to what can be found on a search results page - and content built on a SERP brief will always produce SERP-level output.
Brief architecture is the next lever. A brief that can be fulfilled by someone with no direct experience of the subject will produce commodity content regardless of who writes it. Rewriting the brief so it can only be fulfilled by someone with first-hand knowledge - a specific campaign result, a real client outcome, a dataset the team owns - is the operational shift that moves a content programme from substitutable to defensible. Add a measurement layer on top of those two and you have a programme, not just a production line.
Non-commodity content compounds in a way commodity content cannot. A piece built on proprietary data or genuine first-hand experience builds citations, earns links, and accumulates authority signals over time. The same piece also has a structural advantage against AI Overview displacement: it answers questions the AI cannot reach, because the inputs it draws on exist in your organisation, not in a training dataset.
Frequently asked questions
What is non-commodity content?
Non-commodity content is content that competitors cannot easily replicate because it is built on direct experience, proprietary data, or a viewpoint that requires genuine first-hand knowledge. Google's Danny Sullivan defined it at Google Search Central Toronto (see the Google Search Central blog for Sullivan's published commentary) as content that is unique, specific, and authentic - content that requires genuine first-hand input to produce, using information that is not publicly available from a SERP.
What is commodity content in SEO?
Commodity content in SEO is any content that is structurally substitutable - meaning a competitor could publish something functionally identical from the same brief. It is characterised by generic framing, reliance on secondary sources, and a lack of direct experience or proprietary insight. The content can be well-written and factually accurate and still be commodity if it is interchangeable with what others are publishing.
Is AI-generated content automatically commodity content?
Volume-first AI content strategies produce commodity content by design. AI models draw on existing published information and recombine it competently - the output is useful but substitutable. AI tools add value when shaping knowledge a human already holds, which means the human's direct experience has to enter the process before the AI can do anything non-replicable with it.
What is the relationship between non-commodity content and E-E-A-T?
E-E-A-T is the mechanism Google uses to assess non-commodity content in practice. Commodity content often performs adequately on Expertise but Experience is the dimension it is not designed to address - that signal requires the author to have been genuinely present for what they are describing. Non-commodity content satisfies the Experience signal by grounding claims in direct knowledge rather than synthesised secondary research.
Did Google say all commodity content is bad?
No. Danny Sullivan's framing at Toronto acknowledged that commodity content plays a supporting role - foundational explainers and definitional content serve legitimate navigational purposes for early-stage readers. It contributes scaffolding to a content programme. A content programme built primarily on commodity pages accumulates weak authority signals and is structurally vulnerable to AI Overview displacement.
Why does commodity content struggle more in AI search experiences?
AI Overviews can satisfy generic queries instantly by synthesising information from multiple sources. If your content is substitutable, the AI can answer the same question without sending the user to your page. Non-commodity content - built on proprietary data, direct experience, or genuinely specific insight - answers questions the AI cannot reach, because the inputs do not exist in its training data. That structural advantage is what makes non-commodity content defensible in an AI search environment.
How can a small business create non-commodity content without a big budget?
Knowledge is the asset. A small business typically has direct experience, real client outcomes, and specific operational data that larger competitors lack. The strategy is to build systems that surface what the team knows - expert input sessions and case study templates that force specificity. Shaped into publishable content, that knowledge becomes structurally non-replicable by anyone without the same experience.
How can teams measure whether they are moving away from commodity content?
A practical test is the substitutability check: could a competitor with the same brief and a capable writer publish something functionally identical? If the answer is yes, the piece is commodity. Teams can also track citation rates and link acquisition over time - non-commodity content built on proprietary data or genuine first-hand experience tends to accumulate those signals in ways commodity content does not.
Should I delete my commodity pages?
Deletion is rarely the right first move. Commodity pages that serve a navigational or supporting role in your content architecture can be retained and used to direct readers toward non-commodity content. The more productive intervention is stopping the production of new commodity content and rewriting the brief process so future output is structurally non-replicable. Audit existing pages for consolidation or improvement before defaulting to removal.
Can I still produce non-commodity content with AI tools?
Yes, with the right division of labour. AI handles research synthesis and structural drafting efficiently. The constraint is that AI cannot supply the first-hand knowledge the strategy depends on. A campaign result, a client outcome, a proprietary dataset - that input has to come from a human with direct experience. The AI shapes what that person knows into a publishable piece.