How Google’s Markov Chains Shape Search — And How AI Lets SEO Pros Simulate the Process
Introduction: The hidden maths behind Google’s search results
When you type a query into Google, what you see on the results page looks straightforward: ten blue links, ads at the top, maybe a featured snippet or map pack. But beneath that polished simplicity lies a staggering amount of mathematics, probability theory, and machine learning.
One of the oldest and still most enduring mathematical ideas underpinning Google is something called a Markov chain. This method, first formalised in the early 20th century, models systems where the next state depends only on the current state — a way of saying “what happens next depends only on where you are now.”
For search, Markov chains were revolutionary. Google’s founders Sergey Brin and Larry Page used the concept in the late 1990s to create PageRank, a model of the web as a giant network where each link is a “vote” of authority. Today, more than 25 years later, Google officially confirms that PageRank — the Markov-chain-inspired heart of the original algorithm — is still part of the ranking systems in 2025.
But here’s the twist: SEO companies don’t just rely on hunches or content tweaks anymore. They now use AI-powered simulations to approximate how Google might model authority, clicks, and behaviour — giving them powerful tools to optimise websites in ways that echo, and sometimes anticipate, what happens inside Google’s black box.
This article will explain what Markov chains are, how Google uses them, why they still matter in 2025, and how modern AI gives optimisation experts the ability to run their own simulations — essentially building mini “Google-like” engines to test and refine SEO strategy.
Part 1: What is a Markov chain?
To make sense of why Google leans on Markov processes, let’s strip the maths back to something simple.
Imagine you’re playing a board game like Snakes and Ladders. You roll a dice, land on a square, and your next move depends only on your current position. That’s a Markov chain in action: the probability of your next state depends only on your present state, not the path you took to get there.
Mathematically, a Markov chain is represented by a transition matrix — a grid of probabilities showing how likely it is to move from one state to another. Over time, as you repeat the process, the system stabilises into a stationary distribution: a long-term pattern of where you’ll spend most of your time.
This is the core idea behind PageRank. The “random surfer” model imagines a person clicking links around the web. At each step, they either follow a link on the current page or randomly “teleport” to a new page. Over millions of steps, the probability of where that surfer ends up reflects the relative importance of each page.
In other words: the authority of a page is the probability that a random web surfer lands on it.
Part 2: Google’s PageRank and the Markov revolution
When Brin and Page applied this idea to the early web, it was a breakthrough. Instead of ranking pages just by keywords or metadata, Google could rank them by network authority. The web was treated as a vast graph of billions of nodes (pages) and edges (links), with PageRank measuring which pages mattered most in the long run.
This wasn’t just theory. PageRank powered Google’s rise over AltaVista, Yahoo, and other search engines that relied on more brittle keyword-matching approaches. It was robust, scalable, and mathematically elegant.
How it works (simplified):
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Each web page starts with equal “rank.”
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Pages pass authority to the pages they link to.
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A damping factor (usually ~0.85) models the probability the surfer follows a link vs. teleports.
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Iterating this process millions of times creates a stationary distribution of importance across the entire web.
It’s Markov theory applied to the world’s biggest dataset.
Still alive in 2025
Fast-forward to today: despite layers of AI like BERT, MUM, and Gemini powering semantic understanding, Google’s official Search Ranking Systems Guide (June 2025) still lists PageRank as an active system. That means all the neural intelligence in the world doesn’t erase the usefulness of graph-based authority signals.
The practical takeaway? Links still matter. They may not be the only factor, but their underlying mathematics is still deeply embedded in how search engines work.
Part 3: Beyond links — other ways Google uses Markov processes
Markov chains didn’t stop at PageRank. Over the years, Google and the wider research community have used them (and their cousins) in several important areas:
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Click models and position bias: Not every click means relevance — users click the first result more often simply because it’s at the top. Markov-style models help correct this by simulating how users examine results sequentially.
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Ad attribution (in Ads Data Hub): Markov chains can model user journeys across multiple touchpoints (search ad, YouTube video, display ad) to measure each channel’s contribution to conversion.
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Graph personalization: Variants like Topic-Sensitive PageRank and Personalized PageRank bias the Markov walk towards certain themes or user interests, helping tailor results.
In short: the random-surfer intuition runs deeper than many realise.
Part 4: Why this matters for SEO professionals
Here’s where it gets interesting for SEO companies. While Google guards its exact algorithms, the principles of Markov chains are public. That means optimisation experts can build their own simulations to model:
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Link authority flow: Using graph-based Markov simulations, SEOs can test how adding, removing, or restructuring internal links shifts authority across a site.
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SERP click behaviour: AI models can simulate how real users behave on search results pages — including bounce rates, dwell time, and positional bias — giving clues to how Google might interpret engagement.
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Attribution paths: For clients running ads alongside SEO, Markov attribution models reveal which parts of the funnel are most influential.
The modern twist is that AI supercharges these simulations. Instead of manually calculating transition matrices, SEO companies now use machine learning and generative AI to approximate, test, and even visualise Markov-like processes.
Part 5: How AI lets SEOs simulate Google
1) Graph embeddings & link flow simulations
In the old days, SEOs ran crude PageRank calculators on their backlink profiles. Today, AI models create graph embeddings — vector representations of websites that capture link structure, semantic similarity, and authority flow.
By training embeddings on crawled data, AI can approximate how a Markov process distributes authority, letting SEO pros ask questions like:
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“Which pages hoard authority instead of passing it on?”
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“Where should we add internal links to strengthen topical clusters?”
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“How vulnerable are we if this one backlink disappears?”
2) Behavioural simulations with AI click models
Generative AI can model likely user behaviour on a SERP. For example, an AI click simulator can predict that a long-tail keyword with fewer ads will have higher organic CTRs than a competitive head term.
These models often build on the same logic as Markov click models — sequential decisions, each state depending on the last — but with AI filling in the complexity of human attention patterns.
3) Attribution modelling with hybrid AI–Markov methods
Markov attribution works by removing a channel from the chain and measuring the drop in conversions. AI augments this by layering in contextual data — time of day, user demographics, even sentiment analysis — to make attribution more realistic.
For businesses, this means SEO consultants can provide not just ranking improvements but full-funnel insights into how organic search interacts with paid, social, and referral.
Part 6: A simple worked example for readers
Let’s imagine a small website with four pages:
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Homepage (H)
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Blog (B)
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Product Page (P)
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Contact (C)
The links look like this:
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H links to B and P
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B links back to H
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P links to C
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C links to H
Using a Markov transition matrix, we can calculate the probability of a random surfer being on each page after many steps. Without running the maths in full here, the distribution might stabilise as:
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Homepage: 40%
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Blog: 25%
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Product: 20%
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Contact: 15%
What does this mean in practice? The homepage dominates authority because everything loops back to it. If the goal is to sell products, an SEO expert might recommend adding links from the blog directly to the product page, boosting its share of the stationary distribution.
AI tools let agencies scale this logic to thousands of pages, automatically testing scenarios and predicting impact before changes are made.
Part 7: Why AI is such a natural partner for SEO simulation
Here’s why optimisation experts lean on AI today:
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Scale. A human can draw a four-page graph; AI can map millions of URLs and backlinks in seconds.
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Complexity. Markov models assume “memoryless” transitions, but real users have memory, preferences, and context. AI models add those missing layers.
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Prediction. By training on past data, AI can forecast future SERP shifts, traffic impact, or authority flow — something pure maths can’t do alone.
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Experimentation. AI lets SEOs run what-if scenarios cheaply: “What if we remove this nav bar link?” “What if 10% of our backlinks vanish?”
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Visualisation. Modern AI tools generate interactive graphs and heatmaps, turning abstract probability into intuitive site maps clients can understand.
The end result is that SEO companies no longer operate blind. They run mini-Google simulations, guided by Markov principles and turbocharged by AI.
Part 8: The ethical and practical implications
While these simulations are powerful, they raise questions:
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Opacity vs transparency: Google doesn’t disclose weighting of signals. SEOs using AI simulations must balance insight with the reality that Google’s true algorithm is a moving target.
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Fairness: Over-optimisation risks gaming the system; ethical SEO means using simulations to improve relevance and user experience, not manipulate.
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AI reliability: AI-generated simulations are only as good as their training data. Poorly tuned models can mislead optimisation strategies.
Still, the overall direction is clear: the industry is moving toward evidence-based SEO grounded in simulation and testing, not just hunches.
Part 9: What this means for businesses and website owners
For the average business owner, here’s the bottom line:
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Links and authority still matter. Despite AI revolutions, Google still uses PageRank-like signals.
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User behaviour is part of the puzzle. Clicks, dwell time, and engagement feed into ranking adjustments — which AI models can now predict.
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AI-backed SEO firms have an edge. Agencies using AI to simulate authority and behaviour can make sharper, faster recommendations.
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Content and intent remain king. All the maths in the world still points to one thing: content that matches search intent performs best.
Conclusion: The fusion of old maths and new AI
Markov chains are over a century old. Google turned them into the web’s most powerful ranking signal. And now, in 2025, AI is giving SEO professionals the ability to simulate, visualise, and optimise against these processes like never before.
The result is an SEO industry that is both more scientific and more creative — using AI to peer inside the black box of Google, while still grounded in the timeless maths of probability.
For businesses, the lesson is simple: working with an SEO partner who understands both the Markov logic of authority and the AI tools of modern optimisation is no longer optional — it’s essential for visibility in an AI-dominated search era.
