Rand Fishkin just published the most important piece of primary research the AI visibility industry has seen so far.

His conclusion – that AI tools produce wildly inconsistent brand recommendation lists, making “ranking position” a meaningless metric – is correct, well-evidenced, and long overdue.

But Fishkin stopped one step short of the answer that matters.

He didn’t explore why some brands appear consistently while others don’t, or what would move a brand from inconsistent to consistent visibility. That solution is already formalized, patent pending, and proven in production across 73 million brand profiles.

When I shared this with Fishkin directly, he agreed. The AI models are pulling from a semi-fixed set of options, and the consistency comes from the data. He just didn’t have the bandwidth to dig deeper, which is fair enough, but the digging has been done – I’ve been doing it for a decade.

Here’s what Fishkin found, what it actually means, and what the data proves about what to do about it.

Fishkin’s data killed the myth of AI ranking position

Fishkin and Patrick O’Donnell ran 2,961 prompts across ChatGPT, Claude, and Google AI, asking for brand recommendations across 12 categories. The findings were surprising for most.

Fewer than 1 in 100 runs produced the same list of brands, and fewer than 1 in 1,000 produced the same list in the same order. These are probability engines that generate unique answers every time. Treating them as deterministic ranking systems is – as Fishkin puts it – “provably nonsensical,” and I’ve been saying this since 2022. I’m grateful Fishkin finally proved it with data.

But Fishkin also found something he didn’t fully unpack. Visibility percentage – how often a brand appears across many runs of the same prompt – is statistically meaningful. Some brands showed up almost every time, while others barely appeared at all.

That variance is where the real story lies.

Fishkin acknowledged this but framed it as a better metric to track. The real question isn’t how to measure AI visibility, it’s why some brands achieve consistent visibility and others don’t, and what moves your brand from the inconsistent pile to the consistent pile.

That’s not a tracking problem. It’s a confidence problem.

AI systems are confidence engines, not recommendation engines

AI platforms – ChatGPT, Claude, Google AI, Perplexity, Gemini, all of them – generate every response by sampling from a probability distribution shaped by:

• What the model knows.
• How confidently it knows it.
• What it retrieved at the moment of the query.

When the model is highly confident about an entity’s relevance, that entity appears consistently. When the model is uncertain, the entity sits at a low probability weight in the distribution – included in some samples, excluded in others – not because the selection is random but because the AI doesn’t have enough confidence to commit.

That’s the inconsistency Fishkin documented, and I recognized it immediately because I’ve been tracking exactly this pattern since 2015. 

• City of Hope appearing in 97% of cancer care responses isn’t luck. It’s the result of deep, corroborated, multi-source presence in exactly the data these systems consume. 
• The headphone brands at 55%-77% are in a middle zone – known, but not unambiguously dominant. 
• The brands at 5%-10% have low confidence weight, and the AI includes them in some outputs and not others because it lacks the confidence to commit consistently. 

Confidence isn’t just about what a brand publishes or how it structures its content. It’s about where that brand stands relative to every other entity competing for the same query – a dimension I’ve recently formalized as Topical Position.

I’ve formalized this phenomenon as “cascading confidence” – the cumulative entity trust that builds or decays through every stage of the algorithmic pipeline, from the moment a bot discovers content to the moment an AI generates a recommendation. It’s the throughline concept in a framework I published this week.

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Every piece of content passes through 10 gates before influencing an AI recommendation

The pipeline is called DSCRI-ARGDW – discovered, selected, crawled, rendered, indexed, annotated, recruited, grounded, displayed, and won. That sounds complicated, but I can summarize it in a single question that repeats at every stage: How confident is the system in this content?

• Is this URL worth crawling? 
• Can it be rendered correctly? 
• What entities and relationships does it contain? 
• How sure is the system about those annotations? 
• When the AI needs to answer a question, which annotated content gets pulled from the index? 

Confidence at each stage feeds the next. A URL from a well-structured, fast-rendering, semantically clean site arrives at the annotation stage with high accumulated confidence before a single word of content is analyzed. A URL from a slow, JavaScript-heavy site with inconsistent information arrives with low confidence, even if the actual content is excellent.

This is pipeline attenuation, and here’s where the math gets unforgiving. The relationship is multiplicative, not additive:

• C_final = C_initial × ∏τᵢ

In plain English, the final confidence an AI system has in your brand equals the initial confidence from your entity home multiplied by the transfer coefficient at every stage of the pipeline. The entity home – the canonical web property that anchors your entity in every knowledge graph and every AI model – sets the starting confidence, and then each stage either preserves or erodes it. 

Maintain 90% confidence at each of 10 stages, and end-to-end confidence is 0.9¹⁰ = 35%. At 80% per stage, it’s 0.8¹⁰ = 11%. One weak stage – say 50% at rendering because of heavy JavaScript – drops the total from 35% to 19% even if every other stage is at 90%. One broken stage can undo the work of nine good ones.

This multiplicative principle isn’t new, and it doesn’t belong to anyone. In 2019, I published an article, How Google Universal Search Ranking Works: Darwinism in Search, based on a direct explanation from Google’s Gary Illyes. He described how Google calculates ranking “bids” by multiplying individual factor scores rather than adding them. A zero on any factor kills the entire bid, no matter how strong the other factors are.

Google applies this multiplicative model to ranking factors within a single system, and nobody owns multiplication. But what the cascading confidence framework does is apply this principle across the full 10-stage pipeline, across all three knowledge graphs.

The system provides measurable transfer coefficients at every transition and bottleneck detection that identifies exactly where confidence is leaking. The math is universal, but the application to a multi-stage, multi-graph algorithmic pipeline is the invention.

This complete system is the subject of a patent application I filed with the INPI titled “Système et procédé d’optimisation de la confiance en cascade à travers un pipeline de traitement algorithmique multi-étapes et multi-graphes.” It’s not a metaphor, it’s an engineered system with an intellectual lineage going back seven years to a principle a Google engineer confirmed to me in person.

Fishkin measured the output – the inconsistency of recommendation lists. But the output is a symptom, and the cause is confidence loss at specific stages of this pipeline, compounded across multiple knowledge representations.

You can’t fix inconsistency by measuring it more precisely. You can only fix it by building confidence at every stage.

The corroboration threshold is where AI shifts from hesitant to assertive

There’s a specific transition point where AI behavior changes. I call it the “corroboration threshold” – the minimum number of independent, high-confidence sources corroborating the same conclusion about your brand before the AI commits to including it consistently.

Below the threshold, the AI hedges. It says “claims to be” instead of “is,” it includes a brand in some outputs but not others, and the reason isn’t randomness but insufficient confidence.

The brand sits in the low-confidence zone, where inconsistency is the predictable outcome. Above the threshold, the AI asserts – stating relevance as fact, including the brand consistently, operating with the kind of certainty that produces City of Hope’s 97%.

My data across 73 million brand profiles places this threshold at approximately 2-3 independent, high-confidence sources corroborating the same claim as the entity home. That number is deceptively small because “high-confidence” is doing the heavy lifting – these are sources the algorithm already trusts deeply, including Wikipedia, industry databases, and authoritative media. 

Without those high-authority anchors, the threshold rises considerably because more sources are needed and each carries less individual weight. The threshold isn’t a one-time gate. Once crossed, the confidence compounds with every subsequent corroboration, which is why brands that cross it early pull further ahead over time, while brands that haven’t crossed it yet face an ever-widening gap.

Not identical wording, but equivalent conviction. The entity home states, “X is the leading authority on Y,” two or three independent, authoritative third-party sources confirm it with their own framing, and the AI encodes it as fact.

This fact is visible in my data, and it explains exactly why Fishkin’s experiment produced the results it did. In narrow categories like LA Volvo dealerships or SaaS cloud computing providers – where few brands exist and corroboration is dense – AI responses showed higher pairwise correlation. 

In broad categories like science fiction novels – where thousands of options exist and corroboration is thin – responses were wildly diverse. The corroboration threshold aligns with Fishkin’s findings.

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Authoritas proved that fabricated entities can’t fool AI confidence systems

Authoritas published a study in December 2025 – “Can you fake it till you make it in the age of AI?” – that tested this directly, and the results confirm that Cascading Confidence isn’t just theory. Where Fishkin’s research shows the output problem – inconsistent lists – Authoritas shows the input side.

Authoritas investigated a real-world case where a UK company created 11 entirely fictional “experts” – made-up names, AI-generated headshots, faked credentials. They seeded these personas into more than 600 press articles across UK media, and the question was straightforward: Would AI models treat these fake entities as real experts?

The answer was absolute: Across nine AI models and 55 topic-based questions – “Who are the UK’s leading experts in X?” – zero fake experts appeared in any recommendation. Six hundred press articles, and not a single AI recommendation. That might seem to contradict a threshold of 2-3 sources, but it confirms it. 

The threshold requires independent, high-confidence sources, and 600 press articles from a single seeding campaign are neither independent – they trace to the same origin – nor high-confidence – press mentions sit in the document graph only.

The AI models looked past the surface-level coverage and found no deep entity signals – no entity home, no knowledge graph presence, no conference history, no professional registration, no corroboration from the kind of authoritative sources that actually move the needle.

The fake personas had volume, they had mentions, but what they lacked was cascading confidence – the accumulated trust that builds through every stage of the pipeline. Volume without confidence means inconsistent appearance at best, while confidence without volume still produces recommendations.

AI evaluates confidence — it doesn’t count mentions. Confidence requires multi-source, multi-graph corroboration that fabricated entities fundamentally can’t build.

AI citability concentration increased 293% in under two months

Authoritas used the weighted citability score, or WCS, a metric that measures how much AI engines trust and cite entities, calculated across ChatGPT, Gemini, and Perplexity using cross-context questions.

I have no influence over their data collection or their results. Fishkin’s methodology and Authoritas’ aren’t identical. Fishkin pinged the same query repeatedly to measure variance, while Authoritas tracks varied queries on the same topic. That said, the directional finding is consistent.

Their dataset includes 143 recognized digital marketing experts, with full snapshots from the original study by Laurence O’Toole and Authoritas in December 2025 and their latest measurement on Feb. 2. The pattern across the entire dataset tells a story that goes far beyond individual scores.

• The top 10 experts captured 30.9% of all citability in December. By February, they captured 59.5% – a 92% increase in concentration in under two months.
• The HHI, or Herfindahl-Hirschman Index, the standard measure of market concentration, rose from 0.026 to 0.104 – a 293% increase in concentration. This happened while the total expert pool widened from 123 to 143 tracked entities.

More experts are being cited, the field is getting bigger, and the top is pulling away faster. Dominance is compounding while the long tail grows.

This is cascading confidence at population scale. The experts who actively manage their digital footprint – clean entity home, corroborated claims, consistent narrative across the algorithmic trinity – aren’t just maintaining their position, they’re accelerating away from everyone else.

Each cycle of AI training and retrieval reinforces their advantage – confident entities generate confident AI outputs, which build user trust, which generate positive engagement signals, which further reinforce the AI’s confidence. It’s a flywheel, and once it’s spinning, it becomes very, very hard for competitors to catch up.

At the individual level, the data confirms the mechanism. I lead the dataset at a WCS of 23.50, up from 21.48 in December, a gain of +2.02. That’s not because I’m more famous than everyone else on the list.

It’s because we’ve been systematically building my cascading confidence for years – clean entity home, corroborated claims across the algorithmic trinity, consistent narrative, structured data, deep knowledge graph presence.

I’m the primary test case because I’m in control of all my variables – I have a huge head start. In a future article, I’ll dig into the details of the scores and why the experts have the scores they do.

The pattern across my client base mirrors the population data. Brands that systematically clean their digital footprint, anchor entity confidence through the entity home, and build corroboration across the algorithmic trinity don’t just appear in AI recommendations.

They appear consistently, their advantage compounds over time, and they exit the low-confidence zone to enter the self-reinforcing recommendation set.

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AI retrieves from three knowledge representations simultaneously, not one

AI systems pull from what I call the Three Graphs model – the algorithmic trinity – and understanding this explains why some brands achieve near-universal visibility while others appear sporadically.

• The entity graph, or knowledge graph, contains explicit entities with binary verified edges and low fuzziness – either a brand is in, or it’s not.
• The document graph, or search engine index, contains annotated URLs with scored and ranked edges and medium fuzziness.
• The concept graph, or LLM parametric knowledge, contains learned associations with high fuzziness, and this is where the inconsistency Fishkin documented comes from.

When retrieval systems combine results from multiple sources – and they do, using mechanisms analogous to reciprocal rank fusion – entities present across all three graphs receive a disproportionate boost.

The effect is multiplicative, not additive. A brand that has a strong presence in the knowledge graph and the document index and the concept space gets chosen far more reliably than a brand present in only one.

This explains a pattern Fishkin noticed but didn’t have the framework to interpret – why visibility percentages clustered differently across categories. The brands with near-universal visibility aren’t just “more famous,” they have dense, corroborated presence across all three knowledge representations. The brands in the inconsistent pool are typically present in only one or two. 

The Authoritas fake expert study confirms this from the negative side. The fake personas existed only in the document graph, press articles, with zero entity graph presence and negligible concept graph encoding. One graph out of three, and the AI treated them accordingly.

What I tell every brand after reading Fishkin’s data

Fishkin’s recommendations were cautious – visibility percentage is a reasonable metric, ranking position isn’t, and brands should demand transparent methodology from tracking vendors. All fair, but that’s analyst advice. What follows is practitioner advice, based on doing this work in production.

Stop optimizing outputs and start optimizing inputs

The entire AI tracking industry is fixated on measuring what AI says about you, which is like checking your blood pressure without treating the underlying condition. Measure if it helps, but the work is in building confidence at every stage of the pipeline, and that’s where I focus my clients’ attention from day one.

Start at the entity home

My experience clearly demonstrates that this single intervention produces the fastest measurable results. Your entity home is the canonical web property that should anchor your entity in every knowledge graph and every AI model. If it’s ambiguous, hedging, or contradictory with what third-party sources say about you, it is actively training AI to be uncertain. 

I’ve seen aligning the entity home with third-party corroboration produce measurable changes in bottom-of-funnel AI citation behavior within weeks, and it remains the highest ROI intervention I know.

Cross the corroboration threshold for the critical claims

I ask every client to identify the claims that matter most:

• Who you are.
• What you do.
• Why you’re credible. 

Then, I work with them to ensure each claim is corroborated by at least 2-3 independent, high-authority sources. Not just mentioned, but confirmed with conviction. 

This is what flips AI from “sometimes includes” to “reliably includes,” and I’ve seen it happen often enough to know the threshold is real.

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Build across all three graphs simultaneously

Knowledge graph presence (structured data, entity recognition), document graph presence (indexed, well-annotated content on authoritative sites), and concept graph presence (consistent narrative across the corpus AI trains on) all need attention. 

The Authoritas study showed exactly what happens when a brand exists in only one – the AI treats it accordingly.

Work the pipeline from Gate 1, not Gate 9

Most SEO and GEO advice operates at the display stage, optimizing what AI shows. But if your content is losing confidence at discovery, selection, rendering, or annotation, it will never reach display consistently enough to matter. 

I’ve watched brands spend months on display-stage optimization that produced nothing because the real bottleneck was three stages earlier, and I always start my diagnostic at the beginning of the pipeline, not the end.

Maintain it because the gap is widening

The WCS data across 143 tracked experts shows that AI citability concentration increased 293% in under two months. The experts who maintain their digital footprint are pulling away from everyone else at an accelerating rate. 

Starting now still means starting early, but waiting means competing against entities whose advantage compounds every cycle. This isn’t a one-time project. It’s an ongoing discipline, and the returns compound with every iteration.

Fishkin proved the problem exists. The solution has been in production for a decade.

Fishkin’s research is a gift to the industry. He killed the myth of AI ranking position with data, he validated that visibility percentage, while imperfect, correlates with something real, and he raised the right questions about methodology that the AI tracking vendors should have been answering all along.

But tracking AI visibility without understanding why visibility varies is like tracking a stock price without understanding the business. The price is a signal, and the business is the thing.

AI recommendations are inconsistent when AI systems lack confidence in a brand. They become consistent when that confidence is built deliberately, through:

• The entity home.
• Corroborated claims that cross the corroboration threshold.
• Multi-graph presence.
• Every stage of the pipeline that processes your content before AI ever generates a response.

This isn’t speculation, and the evidence comes from every direction.

The process behind this approach has been under development since 2015 and is formalized in a peer-review-track academic paper. Several related patent applications have been filed in France, covering entity data structuring, prompt assembly, multi-platform coherence measurement, algorithmic barrier construction, and cascading confidence optimization.

The dataset supporting the work spans 25 billion data points across 73 million brand profiles. In tracked populations, shifts in AI citability have been observed — including cases where the top 10 experts increased their share from 31% to 60% in under two months while the overall field expanded. Independent research from Authoritas reports findings that align with this mechanism.

Fishkin proved the problem exists. My focus over the past decade has been on implementing and refining practical responses to it.

This is the first article in a series. The second piece, “What the AI expert rankings actually tell us: 8 archetypes of AI visibility,” examines how the pipeline’s effects manifest across 57 tracked experts. The third, “The ten gates between your content and an AI recommendation,” opens the DSCRI-ARGDW pipeline itself.

Read more at Read More