By
Search Engine Optimization (SEO) is the process of optimizing on-page and off-page factors that impact how high a web page ranks for a specific search term. This is a multi-faceted process that includes optimizing page loading speed, generating a link building strategy, using SEO tools, as well as learning how to reverse engineer Google’s AI by using computational thinking.
Computational thinking is an advanced type of analysis and problem-solving technique that computer programmers use when writing code and algorithms. Computational thinkers will seek the ground truth by breaking down a problem and analyzing it using first principles thinking.
Since Google does not release their secret sauce to anyone, we will rely on computational thinking. We will walk through some pivotal moments in Google’s history that shaped the algorithms that are used, and we will learn why this matters.
We will begin with a book that was published in 2012, called “How to Create a Mind: The Secret of Human Thought Revealed” by renowned futurist, and inventor Ray Kurzweil. This book dissected the human brain, and broke down the ways it works. We learn from the ground up how the brain trains itself using pattern recognition to become a prediction machine, always working at predicting the future, even predicting the next word.
How do humans recognize patterns in every day life? How are these connections formed in the brain? The book begins with understanding hierarchical thinking, this is understanding a structure that is composed of diverse elements that are arranged in a pattern, this arrangement then represents a symbol such as a letter or character, and then this is further arranged into a more advanced pattern such as a word, and eventually a sentence. Eventually these patterns form ideas, and these ideas are transformed into the products that humans are responsible for building.
By emulating the human brain, revealed is a pathway to creating an advanced AI beyond the current capabilities of the neural networks that were around at the time of publishing.
The book was a blueprint for creating an AI that can scale by vacuuming the world’s data, and use its multi-layered pattern recognition processing to parse text, images, audio, and video. A system optimized for upscaling due to the benefits of the cloud and its parallel processing capabilities. In other words there would be no maximum on data input or output.
This book was so pivotal that soon after its publishing the author Ray Kurzweil was hired by Google to become the Director of Engineering focused on machine learning and language processing. A role that perfectly aligned with the book he had written.
It would be impossible to deny how influential this book was to the future of Google, and how they rank websites. This AI book should be mandatory reading for anyone who wishes to become an SEO expert.
Launched in 2010, DeepMind was a hot new startup using a revolutionary new type of AI algorithm that was taking the world by storm, it was called reinforcement learning. DeepMind described it best as:
“We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards.”
By fusing deep learning with reinforcement learning it became a deep reinforcement learning system. By 2013, DeepMind was using these algorithms to rack up victories against human players on Atari 2600 games – And this was achieved by mimicking the human brain and how it learns from training and repetition.
Similar to how a human learns by repetition, whether it is kicking a ball, or playing Tetris, the AI would also learn. The AI’s neural network tracked performance and would incrementally self-improve resulting in stronger move selection in the next iteration.
DeepMind was so dominant in its technological lead that Google had to buy access to the technology. DeepMind was acquired for more than $500 million in 2014.
After the acquisition the AI industry witnessed successive breakthroughs, a type not seen since May 11, 1997, when chess grandmaster Garry Kasparov lost the first game of a six-game match against Deep Blue, a chess-playing computer developed by scientists at IBM.
In 2015, DeepMind refined the algorithm to test it on Atari’s suite of 49 games, and the machine beat human performance on 23 of them.
That was just the beginning, later in 2015 DeepMind began focusing on AlphaGo, a program with the stated aim of defeating a professional Go World Champion. The ancient game of Go, which was first seen in China some 4000 years ago, is considered to be the most challenging game in human history, with its potential 10360 possible moves.
DeepMind used supervised learning to train the AlphaGo system by learning from human players. Soon after, DeepMind made headlines after AlphaGo beat Lee Sedol, the world champion, in a five-game match in March 2016.
Not be outdone, in October, 2017 DeepMind released AlphaGo Zero, a new model with the key differentiator that it required zero human training. Since it did not require human training, it also required no labeling of data, the system essentially used unsupervised learning. AlphaGo Zero rapidly surpassed its predecessor, as described by DeepMind.
“Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0.”
In the meantime, the SEO world was hyper focused on PageRank, the backbone of Google. It begins in 1995, when Larry Page and Sergey Brin were Ph.D. students at Stanford University. The duo began collaborating on a novel research project nicknamed “BackRub”. The goal was ranking web pages into a measure of importance by converting their backlink data. A backlink is quite simply any link from one page to another, similar to this link.
The algorithm was later renamed to PageRank, named after both the term “web page” and co-founder Larry Page. Larry Page and Sergey Brin had the ambitious goal of building a search engine that could power the entire web purely by backlinks.
And it worked.
SEO professionals immediately understood the basics of how google calculates a quality ranking for a web page by using PageRank. Some Savvy black hat SEO entrepreneurs took it a step further, understanding that to scale content, that it might make sense to buy links instead of waiting to acquire them organically.
A new economy emerged around backlinks. Eager website owners who needed to impact search engine rankings would buy links, and in return desperate to monetize websites would sell them links.
The websites who purchased links often overnight invaded Google outranking established brands.
Ranking using this method worked really well for a long time – Until it stopped working, probably around the same time machine learning kicked in and solved the underlying problem. With the introduction of deep reinforcement learning, PageRank would become a ranking variable, not the dominant factor.
By now the SEO community is divided on link buying as a strategy. I personally believe that link buying offers sub-optimal results, and that the best methods to acquire backlinks is based on variables that are industry specific. One legitimate service that I can recommend is called HARO (Help a Reporter Out). The opportunity at HARO is to acquire backlinks by fulfilling media requests.
Established brands never had to worry about sourcing links, since they had the benefits of time working in their favor. The older a website, the more time it has had to collect high quality backlinks. In other words, a search engine ranking was heavily dependent on the age of a website, if you calculate using the metric time = backlinks.
For example, CNN would naturally receive backlinks for a news article due to its brand, its trust, and because it was listed high to begin with – So naturally it gained more backlinks from people researching an article and linking to the first search result they found.
Meaning that higher ranked webpages organically received more backlinks. Unfortunately, this meant new websites were often forced to abuse the backlink algorithm by turning to a backlink marketplace.
In the early 2000s, buying backlinks worked remarkably well and it was a simple process. Link buyers purchased links from high authority websites, often sitewide footer links, or perhaps on a per article basis (often disguised as a guest post), and the sellers desperate to monetize their websites were happy to oblige – Unfortunately, often at the sacrifice of quality.
Eventually the Google talent pool of machine learning engineers understood that coding search engine results by hand was futile, and a lot of PageRank was handwritten coding. Instead they understood that the AI would eventually become responsible with fully calculating the rankings with no to little human interference.
To stay competitive Google uses every tool in their arsenal and this includes deep reinforcement learning – The most advanced type of machine learning algorithm in the world.
This system layered on top of Google’s acquisition of MetaWeb was a gamechanger. The reason the 2010 MetaWeb acquisition was so important is that it reduced the weight that Google placed on keywords. Context was all of a sudden important, this was achieved by using a categorization methodology called ‘entities’. As Fast Company described:
Once Metaweb figures out to which entity you’re referring, it can provide a set of results. It can even combine entities for more complex searches– “actresses over 40” might be one entity, “actresses living in New York City” might be another, and “actresses with a movie currently playing” might be another. “.
This technology was rolled into a major algorithm update called RankBrain that was launched in the spring of 2015. RankBrain focused on understanding context versus being purely keyword based, and RankBrain would also consider environmental contexts (e.g., searcher location) and extrapolate meaning where there had been none before. This was an important update especially for mobile users.
Now that we understand how Google uses these technologies, let’s use computational theory to speculate on how it’s done.
Deep learning is the most commonly used type of machine learning – It would be impossible for Google not to use this algorithm.
Deep learning is influenced significantly by how the human brain operates and it attempts to mirror the brain’s behavior in how it uses pattern recognition to identify, and categorize objects.
For example, if you see the letter a, your brain automatically recognizes the lines and shapes to then identify it as the letter a. The same is applied by the letters ap, your brain automatically attempts to predict the future by coming up with potential words such as app or apple. Other patterns may include numbers, road signs, or identifying a loved one in a crowded airport.
You can think of the interconnections in a deep learning system to be similar to how the human brain operates with the connection of neurons and synapses.
Deep learning is ultimately the term given to machine learning architectures that join many multilayer perceptron’s together, so that there isn’t just one hidden layer but many hidden layers. The “deeper” that the deep neural network is, the more sophisticated patterns the network can learn.
Fully connected networks can be combined with other machine learning functions to create different deep learning architectures.
Google spiders the world’s websites by following hyperlinks (think neurons) that connect websites to one another. This was the original methodology that Google used from day one, and is still in use. Once websites are indexed various types of AI are used to analyze this treasure trove of data.
Google’s system labels the webpages according to various internal metrics, with only minor human input or intervention. An example of an intervention would be the manual removal of a specific URL due to a DMCA Removal Request.
Google engineers are renowned for frustrating attendees at SEO conferences, and this is because Google executives can never properly articulate how Google operates. When questions are asked about why certain websites fail to rank, it’s almost always the same poorly articulated response. The response is so frequent that often attendees preemptively state that they have committed to creating good content for months or even years on end with no positive results.
Predictably, website owners are instructed to focus on building valuable content – An important component, but far from being comprehensive.
This lack of answer is because the executives are incapable of properly answering the question. Google’s algorithm operates in a black box. There’s input, and then output – and that is how deep learning works.
Let’s now return to a ranking penalty that is negatively impacting millions of websites often without the knowledge of the website owner.
Google is not often transparent, PageSpeed Insights is the exception. Websites that fail this speed test will be sent into a penalty box for loading slowly – Especially if mobile users are impacted.
What is suspected is that at some point in the process there’s a decision tree that parses fast websites, versus slow loading (PageSpeed Insights failed) websites. A decision tree is essentially an algorithmic approach which splits the dataset into individual data points based on different criteria. The criteria may be to negatively influence how high a page ranks for mobile versus desktop users.
Hypothetically a penalty could be applied to the natural ranking score. For example, a website that without penalty would rank at #5 may have a -20, -50, or some other unknown variable that will reduce the rank to #25, #55, or another number as selected by the AI.
In the future we may see the end of the PageSpeed Insights, when Google becomes more confident in their AI. This current intervention on speed by Google is dangerous as it may potentially eliminate results that would have been optimal, and it discriminates against the less tech savvy.
It’s a big request to demand that everyone who runs a small business to have the expertise to successfully diagnose and remedy speed test issues. One simple solution would be for Google to simply release a speed optimization plug-in for wordpress users, as wordpress powers 43% of the internet.
Unfortunately, all SEO efforts are in vain if a website fails to pass Google’s PageSpeed Insights. The stakes are nothing less than a website vanishing from Google.
How to pass this test is an article for another time but at a minimum you should verify if your website passes.
Another important technical metric to worry about is a security protocol called SSL (Secure Sockets Layer). This changes the URL of a domain from http to https, and ensure the secure transmission of data. Any website that does not have SSL enabled will be penalized. While there are some exceptions to this rule, ecommerce and financial websites will be most heavily impacted.
Low cost webhosts charge an annual fee for SSL implementation, meanwhile good webhosts such as Siteground issue SSL certificates for free and automatically integrate them.
Another important element on the website is the Meta Title and Meta description. These content fields have an outsized order of importance that may contribute as much to the success or failure of a page as the entire content of that page.
This is because Google has a high probability of selecting the Meta Title and Meta description to showcase in the search results. And this is why it is important to fill out the meta title and meta description field as carefully as possible.
The alternative is Google may choose to ignore the meta title and meta description to instead auto-generate data that it predicts will result in more clicks. If Google predicts poorly what title to auto-generate, this will contribute to less click-throughs by searchers and consequently this contributes to lost search engine rankings.
If Google believes the included meta description is optimized to receive clicks it will showcase it in the search results. Failing this Google grabs a random chunk of text from the website. Often Google selects the best text on the page, the problem is this is the lottery system and Google is consistently bad at choosing what description to select.
Of course if you believe the content on your page is really good, sometimes it makes sense to allow Google to pick the optimized meta description that best matches the user query. We will opt for no meta description for this article as it is content rich, and Google is likely to select a good description.
In the meantime, billions of humans are clicking on the best search results – This is the human-in-the-loop, Google’s last feedback mechanism – And this is where reinforcement learning kicks in.
Reinforcement learning is a machine learning technique that involves training an AI agent through the repetition of actions and associated rewards. A reinforcement learning agent experiments in an environment, taking actions and being rewarded when the correct actions are taken. Over time, the agent learns to take the actions that will maximize its reward.
The reward could be based on a simple computation that calculates the amount of time spent on a recommended page.
If you combine this methodology with a Human-in-the-loop sub-routine this would sound awfully a lot like existing recommender engines that control all aspects of our digital lives such as YouTube, Netflix, Amazon Prime – And if it sounds like how a search engine should operate you are correct.
The Google flywheel improves with each search, humans train the AI by selecting the best result that best answers their query, and the similar query of millions of other users.
The reinforcing learning agent continuously works on self-improving by reinforcing only the most positive interactions between search and delivered search result.
Google measures the amount of time it takes for a user to scan the results page, the URL they click on, and they measure the amount of time spent on the visited website, and they register the return click. This data is then compiled and compared for every website that offers a similar data match, or user experience.
A website with a low retention rate (time spent on site), is then fed by the reinforcement learning system a negative value, and other competing websites are tested to improve the offered rankings. Google is unbiased, assuming there’s no manual intervention, Google eventually provides the desired search results page.
Users are the human-in-the-loop providing Google with free data and become the final component of the deep reinforcement learning system. In exchange for this service, Google offers the end user an opportunity to click on an ad.
The ads outside of generating revenue serve as a secondary ranking factor, floating more data about what makes a user want to click.
Google essentially learns what a user wants. This can be loosely compared to a recommender engine by a video streaming service. In that case a recommender engine would feed a user content that is targeted towards their interests. For example, a user who habitually enjoys a stream of romantic comedies might enjoy some parodies if they share the same comedians.
If we continue with computational thinking we can assume that Google has trained itself to deliver the best results, and this is often achieved by generalizing and satisfying human biases. It would in fact be impossible for Google’s AI to not optimize results that cater to these biases, if it did the results would be sub-optimal.
In other words there is no magic formula, but there are some best practices.
It is the responsibility of the SEO practitioner to recognize the biases that Google seeks that are specific to their industry – And to feed into those biases. For example, someone searching for election poll results without specifying a date, are most likely searching for the most recent results – this is a recency bias. Someone searching for a recipe, most likely does not need the most recent page, and may in fact prefer a recipe that has withstood the test of time.
It is the responsibility of the SEO practitioner to offer visitors the results they are looking for. This is the most sustainable way of ranking in Google.
Website owners must abandon targeting a specific keyword with the expectation that they can deliver whatever they want to the end user. The search result must precisely match the need of the user.
What is a bias? It could be having a domain name that looks high authority, in other words does the domain name match the market you are serving? Having a domain name with the word India in it may discourage USA users from clicking on the URL, due to a nationalism bias of trusting results that originate from a user’s country of residence. Having a one word domain may also give the illusion of authority.
The most important bias is what does a user want to match their search query? Is it an FAQ, a top 10 list, a blog post? This needs to be answered, and the answer is easy to find. You just need to analyze the competition by performing a Google search in your target market.
Compare this to Black Hat SEO, an aggressive method of ranking websites that exploits devious SPAM techniques, including buying backlinks, falsifying backlinks, hacking websites, auto generating social bookmarks at scale, and other dark methodologies that are applied via a network of black hat tools.
Tools that are often repurposed and resold on various search engine marketing forums, products with next to no value and few odds of succeeding. At the moment these tools enable the sellers to become wealthy while they offer minimal value to the end user.
This is why I recommend abandoning Black Hat. Focus your SEO on viewing it from the lens of machine learning. It’s important to understand that every time someone skips a search result to click on a result buried underneath, it’s the human-in-the-loop collaborating with the deep reinforcement learning system. The human is assisting the AI with self-improving, becoming infinitely better as time progresses.
This is a machine learning algorithm that has been trained by more users than any other system in human history.
Google handles 3.8 million searches per minute on average across the globe. That comes out to 228 million searches per hour, 5.6 billion searches per day. That is a lot of data, and this is why it is foolish to attempt black hat SEO. Assuming Google’s AI is going to remain stagnant is foolish, the system is using the Law of Accelerating Returns to exponentially self-improve.
Google’s AI is becoming so powerful that it is conceivable that it could eventually become the first AI to reach Artificial General Intelligence (AGI). An AGI is an intelligence that is able to use transfer learning to master one field to then apply that learned intelligence across multiple domains. While it may be interesting to explore Google’s future AGI efforts, it should be understood that once the process is in motion it is difficult to stop. This is of course speculating towards the future as Google is currently a type of narrow AI, but that is a topic for another article.
Knowing this spending one second more on black hat is a fool’s errand.
If we accept that Google’s AI will continuously self-improve, then we have no choice but to give up on attempting to outsmart Google. Instead, focus on optimizing a website to optimally provide Google specifically what it is looking for.
As described this involves enabling SSL, optimizing page loading speed, and to optimize the Meta Title and Meta Description. To optimize these fields, the Meta Title and Meta Description must be compared to competing websites – Identify the winning elements that result in a high click through rate.
If you optimized being clicked on, the next milestone is creating the best landing page. The goal is a landing page that optimizes user value so much that the average time spent on page outperforms similar competitors who are vying for the top search engine results.
Only by offering the best user experience can a webpage increase in ranking.
So far we have identified these metrics to be the most important:
The landing page is the most difficult element as you are competing against the world. The landing page must load quickly, and must serve everything that is expected, and then surprise the user with more.
It would be easy to fill another 2000 words describing other AI technologies that Google uses, as well as to dig deep further into the rabbit hole of SEO. The intention here is to refocus attention on the most important metrics.
SEO partitioners are so focused on gaming the system that they forget that at the end of the day, the most important element of SEO is giving users as much value as possible.
One way to achieve this is by never allowing important content to grow stale. If in a month I think of an important contribution, it will be added to this article. Google can then identify how fresh the content is, matched with the history of the page delivering value.
If you are still worried about acquiring backlinks, the solution is simple. Respect your visitors time and give them value. The backlinks will come naturally, as users will find value in sharing your content.
The question then shifts to the website owner on how to provide the best user value and user experience.
10 Best AI SEO Tools (June 2025)
Antoine is a visionary leader and founding partner of Unite.AI, driven by an unwavering passion for shaping and promoting the future of AI and robotics. A serial entrepreneur, he believes that AI will be as disruptive to society as electricity, and is often caught raving about the potential of disruptive technologies and AGI.
As a futurist, he is dedicated to exploring how these innovations will shape our world. In addition, he is the founder of Securities.io, a platform focused on investing in cutting-edge technologies that are redefining the future and reshaping entire sectors.
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5 Best Machine Learning & AI Podcasts (June 2025)
How We Can Benefit From Advancing Artificial General Intelligence (AGI)
10 Best AI Tools for Business (June 2025)
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10 Best AI Writing Generators (June 2025)
Advertiser Disclosure: Unite.AI is committed to rigorous editorial standards to provide our readers with accurate information and news. We may receive compensation when you click on links to products we reviewed.
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SEO Optimization: How Google’s AI Works (June 2025) – Unite.AI
/in website SEO, Website Traffic/by Team ZYTBy
Search Engine Optimization (SEO) is the process of optimizing on-page and off-page factors that impact how high a web page ranks for a specific search term. This is a multi-faceted process that includes optimizing page loading speed, generating a link building strategy, using SEO tools, as well as learning how to reverse engineer Google’s AI by using computational thinking.
Computational thinking is an advanced type of analysis and problem-solving technique that computer programmers use when writing code and algorithms. Computational thinkers will seek the ground truth by breaking down a problem and analyzing it using first principles thinking.
Since Google does not release their secret sauce to anyone, we will rely on computational thinking. We will walk through some pivotal moments in Google’s history that shaped the algorithms that are used, and we will learn why this matters.
We will begin with a book that was published in 2012, called “How to Create a Mind: The Secret of Human Thought Revealed” by renowned futurist, and inventor Ray Kurzweil. This book dissected the human brain, and broke down the ways it works. We learn from the ground up how the brain trains itself using pattern recognition to become a prediction machine, always working at predicting the future, even predicting the next word.
How do humans recognize patterns in every day life? How are these connections formed in the brain? The book begins with understanding hierarchical thinking, this is understanding a structure that is composed of diverse elements that are arranged in a pattern, this arrangement then represents a symbol such as a letter or character, and then this is further arranged into a more advanced pattern such as a word, and eventually a sentence. Eventually these patterns form ideas, and these ideas are transformed into the products that humans are responsible for building.
By emulating the human brain, revealed is a pathway to creating an advanced AI beyond the current capabilities of the neural networks that were around at the time of publishing.
The book was a blueprint for creating an AI that can scale by vacuuming the world’s data, and use its multi-layered pattern recognition processing to parse text, images, audio, and video. A system optimized for upscaling due to the benefits of the cloud and its parallel processing capabilities. In other words there would be no maximum on data input or output.
This book was so pivotal that soon after its publishing the author Ray Kurzweil was hired by Google to become the Director of Engineering focused on machine learning and language processing. A role that perfectly aligned with the book he had written.
It would be impossible to deny how influential this book was to the future of Google, and how they rank websites. This AI book should be mandatory reading for anyone who wishes to become an SEO expert.
Launched in 2010, DeepMind was a hot new startup using a revolutionary new type of AI algorithm that was taking the world by storm, it was called reinforcement learning. DeepMind described it best as:
“We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards.”
By fusing deep learning with reinforcement learning it became a deep reinforcement learning system. By 2013, DeepMind was using these algorithms to rack up victories against human players on Atari 2600 games – And this was achieved by mimicking the human brain and how it learns from training and repetition.
Similar to how a human learns by repetition, whether it is kicking a ball, or playing Tetris, the AI would also learn. The AI’s neural network tracked performance and would incrementally self-improve resulting in stronger move selection in the next iteration.
DeepMind was so dominant in its technological lead that Google had to buy access to the technology. DeepMind was acquired for more than $500 million in 2014.
After the acquisition the AI industry witnessed successive breakthroughs, a type not seen since May 11, 1997, when chess grandmaster Garry Kasparov lost the first game of a six-game match against Deep Blue, a chess-playing computer developed by scientists at IBM.
In 2015, DeepMind refined the algorithm to test it on Atari’s suite of 49 games, and the machine beat human performance on 23 of them.
That was just the beginning, later in 2015 DeepMind began focusing on AlphaGo, a program with the stated aim of defeating a professional Go World Champion. The ancient game of Go, which was first seen in China some 4000 years ago, is considered to be the most challenging game in human history, with its potential 10360 possible moves.
DeepMind used supervised learning to train the AlphaGo system by learning from human players. Soon after, DeepMind made headlines after AlphaGo beat Lee Sedol, the world champion, in a five-game match in March 2016.
Not be outdone, in October, 2017 DeepMind released AlphaGo Zero, a new model with the key differentiator that it required zero human training. Since it did not require human training, it also required no labeling of data, the system essentially used unsupervised learning. AlphaGo Zero rapidly surpassed its predecessor, as described by DeepMind.
“Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0.”
In the meantime, the SEO world was hyper focused on PageRank, the backbone of Google. It begins in 1995, when Larry Page and Sergey Brin were Ph.D. students at Stanford University. The duo began collaborating on a novel research project nicknamed “BackRub”. The goal was ranking web pages into a measure of importance by converting their backlink data. A backlink is quite simply any link from one page to another, similar to this link.
The algorithm was later renamed to PageRank, named after both the term “web page” and co-founder Larry Page. Larry Page and Sergey Brin had the ambitious goal of building a search engine that could power the entire web purely by backlinks.
And it worked.
SEO professionals immediately understood the basics of how google calculates a quality ranking for a web page by using PageRank. Some Savvy black hat SEO entrepreneurs took it a step further, understanding that to scale content, that it might make sense to buy links instead of waiting to acquire them organically.
A new economy emerged around backlinks. Eager website owners who needed to impact search engine rankings would buy links, and in return desperate to monetize websites would sell them links.
The websites who purchased links often overnight invaded Google outranking established brands.
Ranking using this method worked really well for a long time – Until it stopped working, probably around the same time machine learning kicked in and solved the underlying problem. With the introduction of deep reinforcement learning, PageRank would become a ranking variable, not the dominant factor.
By now the SEO community is divided on link buying as a strategy. I personally believe that link buying offers sub-optimal results, and that the best methods to acquire backlinks is based on variables that are industry specific. One legitimate service that I can recommend is called HARO (Help a Reporter Out). The opportunity at HARO is to acquire backlinks by fulfilling media requests.
Established brands never had to worry about sourcing links, since they had the benefits of time working in their favor. The older a website, the more time it has had to collect high quality backlinks. In other words, a search engine ranking was heavily dependent on the age of a website, if you calculate using the metric time = backlinks.
For example, CNN would naturally receive backlinks for a news article due to its brand, its trust, and because it was listed high to begin with – So naturally it gained more backlinks from people researching an article and linking to the first search result they found.
Meaning that higher ranked webpages organically received more backlinks. Unfortunately, this meant new websites were often forced to abuse the backlink algorithm by turning to a backlink marketplace.
In the early 2000s, buying backlinks worked remarkably well and it was a simple process. Link buyers purchased links from high authority websites, often sitewide footer links, or perhaps on a per article basis (often disguised as a guest post), and the sellers desperate to monetize their websites were happy to oblige – Unfortunately, often at the sacrifice of quality.
Eventually the Google talent pool of machine learning engineers understood that coding search engine results by hand was futile, and a lot of PageRank was handwritten coding. Instead they understood that the AI would eventually become responsible with fully calculating the rankings with no to little human interference.
To stay competitive Google uses every tool in their arsenal and this includes deep reinforcement learning – The most advanced type of machine learning algorithm in the world.
This system layered on top of Google’s acquisition of MetaWeb was a gamechanger. The reason the 2010 MetaWeb acquisition was so important is that it reduced the weight that Google placed on keywords. Context was all of a sudden important, this was achieved by using a categorization methodology called ‘entities’. As Fast Company described:
Once Metaweb figures out to which entity you’re referring, it can provide a set of results. It can even combine entities for more complex searches– “actresses over 40” might be one entity, “actresses living in New York City” might be another, and “actresses with a movie currently playing” might be another. “.
This technology was rolled into a major algorithm update called RankBrain that was launched in the spring of 2015. RankBrain focused on understanding context versus being purely keyword based, and RankBrain would also consider environmental contexts (e.g., searcher location) and extrapolate meaning where there had been none before. This was an important update especially for mobile users.
Now that we understand how Google uses these technologies, let’s use computational theory to speculate on how it’s done.
Deep learning is the most commonly used type of machine learning – It would be impossible for Google not to use this algorithm.
Deep learning is influenced significantly by how the human brain operates and it attempts to mirror the brain’s behavior in how it uses pattern recognition to identify, and categorize objects.
For example, if you see the letter a, your brain automatically recognizes the lines and shapes to then identify it as the letter a. The same is applied by the letters ap, your brain automatically attempts to predict the future by coming up with potential words such as app or apple. Other patterns may include numbers, road signs, or identifying a loved one in a crowded airport.
You can think of the interconnections in a deep learning system to be similar to how the human brain operates with the connection of neurons and synapses.
Deep learning is ultimately the term given to machine learning architectures that join many multilayer perceptron’s together, so that there isn’t just one hidden layer but many hidden layers. The “deeper” that the deep neural network is, the more sophisticated patterns the network can learn.
Fully connected networks can be combined with other machine learning functions to create different deep learning architectures.
Google spiders the world’s websites by following hyperlinks (think neurons) that connect websites to one another. This was the original methodology that Google used from day one, and is still in use. Once websites are indexed various types of AI are used to analyze this treasure trove of data.
Google’s system labels the webpages according to various internal metrics, with only minor human input or intervention. An example of an intervention would be the manual removal of a specific URL due to a DMCA Removal Request.
Google engineers are renowned for frustrating attendees at SEO conferences, and this is because Google executives can never properly articulate how Google operates. When questions are asked about why certain websites fail to rank, it’s almost always the same poorly articulated response. The response is so frequent that often attendees preemptively state that they have committed to creating good content for months or even years on end with no positive results.
Predictably, website owners are instructed to focus on building valuable content – An important component, but far from being comprehensive.
This lack of answer is because the executives are incapable of properly answering the question. Google’s algorithm operates in a black box. There’s input, and then output – and that is how deep learning works.
Let’s now return to a ranking penalty that is negatively impacting millions of websites often without the knowledge of the website owner.
Google is not often transparent, PageSpeed Insights is the exception. Websites that fail this speed test will be sent into a penalty box for loading slowly – Especially if mobile users are impacted.
What is suspected is that at some point in the process there’s a decision tree that parses fast websites, versus slow loading (PageSpeed Insights failed) websites. A decision tree is essentially an algorithmic approach which splits the dataset into individual data points based on different criteria. The criteria may be to negatively influence how high a page ranks for mobile versus desktop users.
Hypothetically a penalty could be applied to the natural ranking score. For example, a website that without penalty would rank at #5 may have a -20, -50, or some other unknown variable that will reduce the rank to #25, #55, or another number as selected by the AI.
In the future we may see the end of the PageSpeed Insights, when Google becomes more confident in their AI. This current intervention on speed by Google is dangerous as it may potentially eliminate results that would have been optimal, and it discriminates against the less tech savvy.
It’s a big request to demand that everyone who runs a small business to have the expertise to successfully diagnose and remedy speed test issues. One simple solution would be for Google to simply release a speed optimization plug-in for wordpress users, as wordpress powers 43% of the internet.
Unfortunately, all SEO efforts are in vain if a website fails to pass Google’s PageSpeed Insights. The stakes are nothing less than a website vanishing from Google.
How to pass this test is an article for another time but at a minimum you should verify if your website passes.
Another important technical metric to worry about is a security protocol called SSL (Secure Sockets Layer). This changes the URL of a domain from http to https, and ensure the secure transmission of data. Any website that does not have SSL enabled will be penalized. While there are some exceptions to this rule, ecommerce and financial websites will be most heavily impacted.
Low cost webhosts charge an annual fee for SSL implementation, meanwhile good webhosts such as Siteground issue SSL certificates for free and automatically integrate them.
Another important element on the website is the Meta Title and Meta description. These content fields have an outsized order of importance that may contribute as much to the success or failure of a page as the entire content of that page.
This is because Google has a high probability of selecting the Meta Title and Meta description to showcase in the search results. And this is why it is important to fill out the meta title and meta description field as carefully as possible.
The alternative is Google may choose to ignore the meta title and meta description to instead auto-generate data that it predicts will result in more clicks. If Google predicts poorly what title to auto-generate, this will contribute to less click-throughs by searchers and consequently this contributes to lost search engine rankings.
If Google believes the included meta description is optimized to receive clicks it will showcase it in the search results. Failing this Google grabs a random chunk of text from the website. Often Google selects the best text on the page, the problem is this is the lottery system and Google is consistently bad at choosing what description to select.
Of course if you believe the content on your page is really good, sometimes it makes sense to allow Google to pick the optimized meta description that best matches the user query. We will opt for no meta description for this article as it is content rich, and Google is likely to select a good description.
In the meantime, billions of humans are clicking on the best search results – This is the human-in-the-loop, Google’s last feedback mechanism – And this is where reinforcement learning kicks in.
Reinforcement learning is a machine learning technique that involves training an AI agent through the repetition of actions and associated rewards. A reinforcement learning agent experiments in an environment, taking actions and being rewarded when the correct actions are taken. Over time, the agent learns to take the actions that will maximize its reward.
The reward could be based on a simple computation that calculates the amount of time spent on a recommended page.
If you combine this methodology with a Human-in-the-loop sub-routine this would sound awfully a lot like existing recommender engines that control all aspects of our digital lives such as YouTube, Netflix, Amazon Prime – And if it sounds like how a search engine should operate you are correct.
The Google flywheel improves with each search, humans train the AI by selecting the best result that best answers their query, and the similar query of millions of other users.
The reinforcing learning agent continuously works on self-improving by reinforcing only the most positive interactions between search and delivered search result.
Google measures the amount of time it takes for a user to scan the results page, the URL they click on, and they measure the amount of time spent on the visited website, and they register the return click. This data is then compiled and compared for every website that offers a similar data match, or user experience.
A website with a low retention rate (time spent on site), is then fed by the reinforcement learning system a negative value, and other competing websites are tested to improve the offered rankings. Google is unbiased, assuming there’s no manual intervention, Google eventually provides the desired search results page.
Users are the human-in-the-loop providing Google with free data and become the final component of the deep reinforcement learning system. In exchange for this service, Google offers the end user an opportunity to click on an ad.
The ads outside of generating revenue serve as a secondary ranking factor, floating more data about what makes a user want to click.
Google essentially learns what a user wants. This can be loosely compared to a recommender engine by a video streaming service. In that case a recommender engine would feed a user content that is targeted towards their interests. For example, a user who habitually enjoys a stream of romantic comedies might enjoy some parodies if they share the same comedians.
If we continue with computational thinking we can assume that Google has trained itself to deliver the best results, and this is often achieved by generalizing and satisfying human biases. It would in fact be impossible for Google’s AI to not optimize results that cater to these biases, if it did the results would be sub-optimal.
In other words there is no magic formula, but there are some best practices.
It is the responsibility of the SEO practitioner to recognize the biases that Google seeks that are specific to their industry – And to feed into those biases. For example, someone searching for election poll results without specifying a date, are most likely searching for the most recent results – this is a recency bias. Someone searching for a recipe, most likely does not need the most recent page, and may in fact prefer a recipe that has withstood the test of time.
It is the responsibility of the SEO practitioner to offer visitors the results they are looking for. This is the most sustainable way of ranking in Google.
Website owners must abandon targeting a specific keyword with the expectation that they can deliver whatever they want to the end user. The search result must precisely match the need of the user.
What is a bias? It could be having a domain name that looks high authority, in other words does the domain name match the market you are serving? Having a domain name with the word India in it may discourage USA users from clicking on the URL, due to a nationalism bias of trusting results that originate from a user’s country of residence. Having a one word domain may also give the illusion of authority.
The most important bias is what does a user want to match their search query? Is it an FAQ, a top 10 list, a blog post? This needs to be answered, and the answer is easy to find. You just need to analyze the competition by performing a Google search in your target market.
Compare this to Black Hat SEO, an aggressive method of ranking websites that exploits devious SPAM techniques, including buying backlinks, falsifying backlinks, hacking websites, auto generating social bookmarks at scale, and other dark methodologies that are applied via a network of black hat tools.
Tools that are often repurposed and resold on various search engine marketing forums, products with next to no value and few odds of succeeding. At the moment these tools enable the sellers to become wealthy while they offer minimal value to the end user.
This is why I recommend abandoning Black Hat. Focus your SEO on viewing it from the lens of machine learning. It’s important to understand that every time someone skips a search result to click on a result buried underneath, it’s the human-in-the-loop collaborating with the deep reinforcement learning system. The human is assisting the AI with self-improving, becoming infinitely better as time progresses.
This is a machine learning algorithm that has been trained by more users than any other system in human history.
Google handles 3.8 million searches per minute on average across the globe. That comes out to 228 million searches per hour, 5.6 billion searches per day. That is a lot of data, and this is why it is foolish to attempt black hat SEO. Assuming Google’s AI is going to remain stagnant is foolish, the system is using the Law of Accelerating Returns to exponentially self-improve.
Google’s AI is becoming so powerful that it is conceivable that it could eventually become the first AI to reach Artificial General Intelligence (AGI). An AGI is an intelligence that is able to use transfer learning to master one field to then apply that learned intelligence across multiple domains. While it may be interesting to explore Google’s future AGI efforts, it should be understood that once the process is in motion it is difficult to stop. This is of course speculating towards the future as Google is currently a type of narrow AI, but that is a topic for another article.
Knowing this spending one second more on black hat is a fool’s errand.
If we accept that Google’s AI will continuously self-improve, then we have no choice but to give up on attempting to outsmart Google. Instead, focus on optimizing a website to optimally provide Google specifically what it is looking for.
As described this involves enabling SSL, optimizing page loading speed, and to optimize the Meta Title and Meta Description. To optimize these fields, the Meta Title and Meta Description must be compared to competing websites – Identify the winning elements that result in a high click through rate.
If you optimized being clicked on, the next milestone is creating the best landing page. The goal is a landing page that optimizes user value so much that the average time spent on page outperforms similar competitors who are vying for the top search engine results.
Only by offering the best user experience can a webpage increase in ranking.
So far we have identified these metrics to be the most important:
The landing page is the most difficult element as you are competing against the world. The landing page must load quickly, and must serve everything that is expected, and then surprise the user with more.
It would be easy to fill another 2000 words describing other AI technologies that Google uses, as well as to dig deep further into the rabbit hole of SEO. The intention here is to refocus attention on the most important metrics.
SEO partitioners are so focused on gaming the system that they forget that at the end of the day, the most important element of SEO is giving users as much value as possible.
One way to achieve this is by never allowing important content to grow stale. If in a month I think of an important contribution, it will be added to this article. Google can then identify how fresh the content is, matched with the history of the page delivering value.
If you are still worried about acquiring backlinks, the solution is simple. Respect your visitors time and give them value. The backlinks will come naturally, as users will find value in sharing your content.
The question then shifts to the website owner on how to provide the best user value and user experience.
10 Best AI SEO Tools (June 2025)
Antoine is a visionary leader and founding partner of Unite.AI, driven by an unwavering passion for shaping and promoting the future of AI and robotics. A serial entrepreneur, he believes that AI will be as disruptive to society as electricity, and is often caught raving about the potential of disruptive technologies and AGI.
As a futurist, he is dedicated to exploring how these innovations will shape our world. In addition, he is the founder of Securities.io, a platform focused on investing in cutting-edge technologies that are redefining the future and reshaping entire sectors.
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Advertiser Disclosure: Unite.AI is committed to rigorous editorial standards to provide our readers with accurate information and news. We may receive compensation when you click on links to products we reviewed.
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Best AI Tools for Content Writing 2025: Revolutionizing Your Creative Process – Zoom Bangla News
/in website SEO, Website Traffic/by Team ZYTIn the rapidly evolving world of digital content, 2025 is poised to be a transformative year, redefining how creators engage with their audience. Best AI Tools for Content Writing 2025 are at the forefront of this change, offering innovative solutions to elevate creativity and efficiency. Imagine a world where you can effortlessly brainstorm, generate, and refine content—all while maintaining your unique voice. As competition intensifies, the most successful content creators will be those who embrace these cutting-edge tools to craft compelling narratives that resonate on a deeper, emotional level.
As we delve into the Best AI Tools for Content Writing 2025, their role in transforming the creative process becomes crystal clear. These tools are not just about automating writing; they offer enhanced creativity, ensuring the content remains engaging and relevant. From sophisticated algorithms capable of understanding context to tools designed for seamless workflow integration, 2025 promises to redefine the content creation landscape. Supporting keywords such as “AI-driven writing assistant” and “content creation technology” are naturally intertwined throughout this discussion, enriching the narrative.
With advancements in natural language processing, AI tools have become more intuitive, meeting the needs of modern writers better than ever. Imagine an AI that can effortlessly switch tone, adapt to different styles, and provide real-time suggestions, all while training from your past work for personalized outputs. This not only streamlines the writing process but also enhances the quality and consistency of the content. As these technologies continue to evolve, the way we approach content creation will change fundamentally, necessitating a strategic integration of these AI tools to stay ahead of the curve.
Beyond merely facilitating content generation, these AI tools play an integral role in optimizing content for a global audience. With capabilities to translate and localize content with precision, AI tools are not just limited to language translation but extend to cultural nuances, ensuring messages resonate well across different demographics. This evolution supports global outreach efforts, allowing content creators to connect with diverse audience segments more authentically.
The rise of Best AI Tools for Content Writing 2025 is also linked directly to innovative technologies shaping content creation. As data analytics continue to drive personalization, AI tools are becoming more adept at curating content that is both relevant and timely. Keywords like “personalized content strategies” and “data-driven insights” find their way naturally into this conversation too, highlighting the depth of integration possible with these tools.
Imagine using AI to identify market trends shifts within your niche, automatically generating content that aligns with these emerging trends, ensuring your audience feels consistently engaged and updated. This capability transcends mere automation, offering a level of personalization that was once unimaginable. By leveraging these technologies, creators can develop content strategies that are not only aligned with real-time data but also anticipate market needs, setting them apart from the competition.
Embracing these tools means recognizing the potential for AI to not only assist but also inspire. These tools can help break through creative blocks, offering new perspectives and insights that might not have been considered otherwise. They simulate collaborative environments, offering suggestions akin to brainstorming sessions, which can foster innovation and out-of-the-box thinking. In this context, AI becomes a partner in the creative process, contributing to a richer and more dynamic content ecosystem.
Within the grander scheme, the practical implications of AI in content writing cannot be overstated. These technologies offer scalability for content creators, enabling them to produce a higher volume of quality content without compromising on time or resources. By automating mundane tasks, writers can dedicate more time to strategic aspects, resulting in content that is not only abundant but refined.
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As we move forward, it's essential to navigate this landscape with an awareness of ethical considerations. Maintaining authenticity and ensuring content generated remains factual and unbiased will be paramount. Creators must balance the benefits of AI with a commitment to ethical practices, maintaining the integrity of their work.
Looking ahead, embracing the Best AI Tools for Content Writing 2025 represents a commitment to evolution and growth. By incorporating these technologies, content creators empower themselves to meet the demands of an ever-changing audience landscape, ensuring their messages are not only heard but felt. With these tools, the future of content creation is not only bright but boundlessly creative.
What are the Best AI Tools for Content Writing in 2025?
In 2025, some of the most highly regarded AI tools for content writing include advanced algorithms capable of context understanding and seamless integration with workflows. These tools offer enhanced personalization, language translation, and creative suggestions, making them indispensable for modern content creators.
How do AI tools enhance content personalization?
AI tools use data analytics to tailor content strategies, allowing creators to generate personalized content that resonates with specific audience segments. By leveraging insights from past interactions and real-time data, AI tools can craft narratives that are both relevant and engaging.
Can AI tools replace human writers?
While AI tools offer significant benefits in terms of speed and efficiency, they are best viewed as complementary tools rather than replacements for human writers. These tools facilitate the creative process, ensure consistency, and handle repetitive tasks, allowing writers to focus on strategic and creative aspects.
What ethical considerations should be noted with AI-generated content?
Content creators must ensure that AI-generated content remains factual, unbiased, and authentic. Ethical considerations include maintaining the integrity of content, safeguarding against misinformation, and ensuring that AI-promoted messages align with brand values and ethical standards.
How do AI content tools handle language translation and localization?
AI content tools have advanced capabilities to translate and localize content with cultural nuances in mind. This means they can adapt content beyond mere language translation, ensuring messages resonate across different demographics effectively, making them ideal for global outreach efforts.
Disclaimer: This article is intended for informational purposes only and should not be construed as professional advice. Content accuracy is checked to the best of our ability but is subject to change. Always verify directly with official sources.
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Future of SEO: 5 Key SEO Trends (2025 & 2026) – Exploding Topics
/in website SEO, Website Traffic/by Team ZYTFuture of SEO: 5 Key SEO Trends (2025 & 2026) Exploding Topics
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Best Free AI Content Creators: Top Tools and Features – Simplilearn.com
/in website SEO, Website Traffic/by Team ZYTBest Free AI Content Creators: Top Tools and Features Simplilearn.com
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How PR Can Win in AI Search: SEO Strategies for the LLM Era – prnewsonline.com
/in website SEO, Website Traffic/by Team ZYTBy Nicole Schuman
Mirza Germovic, SVP of AI Solutions & Advisory at Edelman and Whitney Hart, Chief Strategy Officer, Avenue Z explained why and how certain media sources are more likely to appear in AI search results during the PRNEWS Pro workshop “Artificial Intelligence for PR.”
Whitney Hart gives three actionable takeaways for anyone trying to gain visibility via Large Language Models such as ChatGPT, AI Overviews, Open AI and other AI search platforms.
Watch the segment here, or watch the full session, “AI’s Influence on SEO – What PR Teams Should Know,” by following the link.
Full transcript:
[WHITNEY HART] So to dive into the media side more carefully, since that’s what we’re here to talk about today, news and media sources. ChatGPT is most commonly linking to the nine media sources linked on the right. And this is according to similar web from a March, 2025 study.
So we see that top news and media sites like Reuters, New York Post, New York Times, Wall Street Journal, Forbes, are all benefiting from traffic being driven to their sites as a result of their large language model answers that they’re delivering to individuals.
And as Mirza also alluded to earlier, we are seeing that OpenAI has partnered with all of the different outlets that are listed on the right, everything from the Associated Press back in July of 2023 through the summer of 24 with the FT, the Atlantic, News Corp, Reddit, Time, all the way to February of last month with Guardian Media Group in the UK. So, ChatGPT is looking to understand and can peek behind the paywall of all of these different outlets that are listed here.
So what does that mean for us? Specifically when press releases are published about these partnerships, OpenAI stipulates that they receive access to and current content, including content behind the paywall. And in exchange, ChatGP provides answers, including attribution and links to the full articles for transparency. So we are seeing that reputable media placements are driving results. And increasingly, those results are from those outlets with which OpenAI has crafted a formal partnership.
Right now we’re seeing chat GPT, Perplexity, Gemini, Co-pilot leading, but you never know when another deep leak is gonna hop into the headline and that’s the new thing we need to throw some attention to.
I would also recommend that you go audit the content on your website. A lot of that content on your website has been created around SEO keywords and it hasn’t necessarily been thought through through the lens of PR messaging frameworks. So I’d recommend that you take some time, audit your website content, blog content, things like this, and assess where you think edits need to be made and bring that back to the SEO team, to those relationships that you’re forging and see what can be done.
And last but not least, audit coverage. Look at the coverage from the last two years and assess how strategies potentially need to change for your new audience, the AI bot.
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SaaS SEO Guide: Rank #1 In Google – Exploding Topics
/in website SEO, Website Traffic/by Team ZYTSaaS SEO Guide: Rank #1 In Google Exploding Topics
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The Google Phone app has received a new design in the Material 3 Expressive style – Mezha.Media
/in website SEO, Website Traffic/by Team ZYTThe Google Phone app has received a new design in the Material 3 Expressive style Mezha.Media
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Ask An SEO: How AI Is Changing Affiliate Strategies – Search Engine Journal
/in website SEO, Website Traffic/by Team ZYTDownload your cheat sheet and checklist to start building content that works harder.
Large AI Overviews on SERPs are affecting visibility and causing a dramatic decrease in traffic.
In this actionable session, Nick Gallagher, SEO Lead at Conductor, as he gives you actionable SEO guidance in this new era of search engine results page (SERPs).
Large AI Overviews on SERPs are affecting visibility and causing a dramatic decrease in traffic.
This template is your no-nonsense roadmap to a flexible, agile social media strategy.
Large AI Overviews on SERPs are affecting visibility and causing a dramatic decrease in traffic.
In this week’s Ask an SEO, we break down the impact of AI on affiliate marketing, and how to adjust your approach for better results.
This week’s Ask an SEO question about affiliate strategies comes from Mike R:
“How is AI changing affiliate marketing strategy in 2025? I’m concerned my current approach will become obsolete but don’t know which new techniques are actually worth adopting.”
Great question, Mike. I’m seeing a few trends and strategies that are changing, for the better and for the worse.
When AI is used properly in the affiliate marketing channel, it can help businesses and brands grow.
If any of the three types of businesses (defined below) in affiliate marketing use it in a way that AI and large language models are not ready for “yet,” it can backfire.
I’m answering this question in three parts, as I’m unsure which side of the industry you’re on.
For the record: The affiliate channel is not at risk (i.e., affiliate marketing is not dead) because affiliate marketing is more than a content website that creates a list or writes a review, and coupon sites intercept the end of sale.
Affiliate marketing is a mix of all marketing channels, including email, SMS, online and offline communities, PPC, media buying, and even print media.
It is not going to be as impacted by AI as SEO and content marketing – and in many ways, it will likely grow and scale from it.
Affiliates are the party that promotes another brand in hopes of earning a commission.
Here’s some of what I’m seeing regarding the use of AI and its impact on affiliate revenue.
Programmatic SEO is not new, and using LLMs to create content or lists is burning what were quality sites to the ground.
It is almost never a good idea; it doesn’t matter if AI can spin up content and get it publish-ready in minutes.
In the early 2000s, affiliates and SEO professionals would use pre-AI article spinners to create massive quantities of content from one or two professionally written and fact-checked articles, then publish them to blogs and third-party publishing platforms like Squidoo.
This is equivalent to affiliates publishing their content on Reddit or LinkedIn Pulse to rank it.
The algorithms caught up and penalized the affiliate websites. Squidoo and some of the third-party platforms managed to stay afloat as they had trust and a strong user base for a while.
Next, PHP became the go-to for programmatic SEO, and affiliates would generate shopping lists or pages with unique mixes of products and descriptions via merchant data feeds and network-provided tools. Then, these got penalized. Again, nothing new.
Media companies have been getting penalized and devalued for years for this, and plenty of content creators, too.
If an affiliate manager is telling you to use LLMs to create content, or someone is using LLMs and AI to do programmatic SEO, look for advice elsewhere.
I’ve watched multiple quality sites fall since ChatGPT, Perplexity, and others began writing and spinning their content.
In traditional affiliate marketing, if an affiliate is not making sales, even if they send quality traffic, they get ignored. LLMs have changed this 100%.
I’ve seen affiliates, including bloggers, YouTubers, forums, and social media influencers, are being sourced and cited by AI systems.
If the brand is not on the content being used for fact-checking (grounding) and sourcing, the brands begin to disappear from outputs and results. I’m seeing this firsthand.
Not getting traffic or sales, or being number seven to 10 on a list, now has value. The citations and mentions from the resources that LLMs trust can help your brand gain visibility in AI.
Affiliates can and should begin charging extra fees for these placements until the LLMs begin penalizing or ignoring pay-to-play content.
We’re likely a couple of years away from their algorithms being anywhere near that advanced, so it is a prime opportunity while Google is reducing traffic to publishers via AI Overviews.
I think coupon sites are going to take a substantial hit, as AI is starting to create its own lists of coupons that work.
It also includes where and how to save, where to shop, and current deals on specific products. For example, “I want to buy a pair of Asics Kayano 32 men’s running shoes and get them on sale. Where can I find a deal?”
Right now, Google’s AI Overviews are populating lists of where to find deals, and it is showing the coupon sites as the sources to the right. These sites are likely getting clicks now.
I’ve seen ChatGPT pull the codes directly and preventing the need to click to the coupon website and set their affiliate tracking. It does show the website it came from, though – just no reason to click since you get the code in the output.
One interesting thing is that ChatGPT may pull in vanity codes.
The output from ChatGPT featuring these could give an influencer who was sourced for the code or a coupon site credit for their sales, throwing attribution off, because it was the coupon that triggered the commission, even though the user was using the LLM.
The influencer did not have anything to do with this transaction, but they’ll be getting credit.
The brand may now pay more money to the influencer, when, in reality, it should be ChatGPT – that is where the customers are, not the influencer.
By showing where to find the deals and which deals are available by product (not brand), AI eliminates one of the deal and coupon site’s top-funnel traffic strategies to brands.
The biggest hit I see coupon sites taking is ranking in search engines for “brand + coupon” for the last-second click from someone who is already in the brand’s shopping cart.
If Google AI Overviews creates its own coupon lists as the output, like ChatGPT is doing, there is no reason to click on a coupon website and click their affiliate links.
But, don’t count deal and coupon sites out. They still have email lists and social media accounts that can drive top-funnel traffic, and they can reintroduce customers who have forgotten about you by utilizing their own internal databases of shoppers.
These are the people who manage programs by recruiting affiliates into the program, giving the affiliates the tools they need, and ensuring the data on the network is tracked and accurate so the brands being promoted have the sales and touchpoints they’re looking for.
Some managers hit the panic button because they relied on content sites and publishers who have SEO rankings, but AI Overviews is using affiliate and publisher content and not sending the same amount of traffic to the publishers.
This reduces the number of clicks and traffic. The publishers are still driving traffic, but it is coming in via Google and not the affiliate channel.
With that said, affiliate managers can shift their focus to channels not as impacted by AI Overviews, including:
From seeing this on a daily basis, it appears that high-quality publisher accounts are being created en masse as fronts for fraud and fake affiliate accounts.
I’ve had conversations with people hired by the fake affiliate account who are being paid to talk to the affiliate manager, so it makes these sites look even more legit. We’ll have back-and-forth emails, and in some cases, a call.
Once the traffic and sales start, it turns out to be stolen credit cards or program violations. In some instances, the person or websites they applied with no longer exist.
Interestingly, when they activate a year later, thinking you forgot about them, magically, the site reappears when they know you’re not checking.
Always evaluate a site, and if the content is being generated by LLMs or AI, it may be best to reject it and reduce the risk of a fake account.
AI content may rank temporarily, but this is not a long-term strategy. If your brand is being written about by AI and spun out to a site via programmatic SEO, there is a reasonable chance that the details won’t be as factual or as on-brand as they should be.
An affiliate who cannot take the time to create good content and use AI to edit, versus using AI to create and then edit, should not be trusted in your affiliate program.
When your affiliates are generating content or fact-checking via LLMs and AI, they’re not doing their jobs as your partners to promote your program factually, with correct talking points, and following brand guidelines.
There’s a reasonable chance that incorrect claims about financial products, medical treatments, or even books to buy and read will be in the content you, as a brand, are paying to have made.
Even if you’re paying on a performance basis, you are approving this content to be live and represent your brand. This is why affiliates in your program using AI to create content are a high risk.
Set rules and enforce them so that your brand cannot be included in any AI-created content, or remove the affiliate from your program until they’re ready to treat your brand or your clients’ brands with the same care as you do.
One interesting use of AI for affiliate management is merchant and affiliate matching using machine learning and AI by agencies and larger brands.
Just because a partner does well in one vertical or with one affiliate program that has a similar audience, it does not mean it is a good match for others.
One exception to using AI for matching is to build a list of potential partners from a database. But automatically approving that list because the output creates a list is problematic.
Each affiliate that is recommended still needs to be vetted by hand to make sure they meet the requirements of the new program.
Some of the best uses of AI, especially LLMs, have been building lists of potential partners.
You can train GPTs to validate the lists, remove current partners so you don’t accidentally email or call them, do a gap analysis, and even customize the recruitment email to a very strong degree.
No, it isn’t perfect, but you can save hours each week from the manual tasks of discovery, validation, and outreach.
The recruitment emails still need to be reviewed and sent manually, but it is a massive time-saver.
We manually review every email before it goes out and have to do a decent chunk of rewriting, but we’re saving large amounts of time, too.
We also pre-schedule the emails using a database tool, but we’ve slowly begun implementing new discovery and drafting methods, and they’re turning out to be fantastic.
I was a non-believer in AI for this at first, but now I’m about ready to double down, especially as the systems advance.
These are the tracking and payment platforms that power the affiliate programs.
Affiliates rely on them to accurately record sales and release payments.
Affiliate managers use them to track progress, simplify paying partners around the world, and generate reports based on the key performance indicators (KPIs) their company uses.
All of the networks we’re working on have an influx of AI-generated sites. I’ve talked to agencies and managers on the ones we don’t work on, and they’re seeing the same.
The networks would be wise to add filters and create an alert for affiliate managers to let them know if the affiliate is human or AI, meaning that AI would be a website and promotional method without quality control.
There are no advanced controls in place on any networks that I’ve seen specifically for AI affiliates. But most networks do have compliance teams to which you can report fake accounts.
From the networks I’ve talked to, they’re working on solutions to help detect and reject these sites, but it is a massive problem because they’re being generated at high volumes, and some are really hard to detect.
The spammers and scammers are getting smarter, and AI has given them a new advantage.
This is a double-edged sword. Networks have more data than any affiliate agency, and they may be best suited to try partner and program matching algorithms.
They can create a list of programs that an affiliate may want to test, or a list of partners a program manager can pay to recruit based on program goals and dimensions.
The downside is that programs spend countless hours recruiting partners for their programs. Networks doing matching and recruitment take that work and give it for free to that program’s competitors.
A second downside is that affiliates get bombarded with program requests, and this can cause that to skyrocket, making it harder to get them to open emails, including program updates and newsletters.
Once they start ignoring emails because of too many, you may not get compliance issues fixed or promotions that would normally have benefited both parties.
One of the most beneficial things a network can do, but none are currently doing on a mass scale (some are starting to, and it’s looking promising), is to use AI to create custom reports for affiliate programs. These could be charts and graphs on trends over XYZ years.
Another is a gap analysis of products that get bundled together by type of affiliate, and then which similar affiliates already in the program don’t have a specific SKU in their orders.
The manager can recommend pre-selling the SKU within the content that drives the sale, or adding that specific SKU as an upsell to any customer who came from that affiliate’s link, based on the affiliate ID passed in the URL.
It can show trends where there are cross-channel (SEO, email, PPC, SMS, etc) touchpoints and how it modifies seasonally, annually, and if the goal creates more or less sales for the affiliate channel or company as a whole.
One important thing to remember is that not all affiliate networks offer true cross-channel reporting. Multiple only offer it once the user has clicked an affiliate link.
AI is going to be amazing and horrible for each of the three entities above that make up the affiliate marketing channel.
If used correctly, it can save time, increase efficiency, and create more meaningful strategies.
At the same time, it could result in violations of a program’s Terms of Service (TOS), steal traffic from publishers, and harm multiple types of businesses.
More Resources:
Featured Image: Paulo Bobita/Search Engine Journal
Adam Riemer is an award winning digital marketing strategist, keynote speaker, affiliate manager, and growth consultant with more than 20+ …
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The importance of managing your SEO strategy in a safe way – Hackread
/in website SEO, Website Traffic/by Team ZYTAs SEO leans towards AI, site owners are more in need of third-party tools, and agencies and updating their own strategy. These fast changes to stay on top of optimisation can become a hotbed for security mistakes, particularly as last-ditch desperation can kick in when you are tanking in the rankings.
Brand integrity, personal data, strategy, and proprietary data must all be protected, along with having a strategy that minimises risks like being penalised by Google.
Unsafe SEO can come in a few forms. Black-hat practices can present a big risk, as Google may penalise your rankings. For example, producing AI content en masse to maximise the number of pages you have within a niche can bloat your sitemap, but may also be flagged outright by Google. Or, the overuse of backlinks to low-quality, irrelevant sites.
In 2025, SEO performance is leaning more towards trustworthiness, and so many turn to agencies to help build backlinks. If such agencies are providing data or partners to drive SEO, it must be reliable. The collaboration presents another risk, as both you and the agency must protect your personal data, but also the campaign strategy and keywords, which an attack could target.
Using insecure third-party tools, such as Chrome Extensions for SERP analysis, can also pose a security risk if they collect and log your data.
A resilient and secure SEO strategy in 2025 has a few core pillars. Firstly, to avoid being penalised by Google, there must be a commitment to acquiring only high-authority and contextually relevant backlinks. Adhering to guidelines and ethical practices is the bread and butter of SEO risk management.
The selection of partners and service providers should involve rigorous due diligence, prioritising those with verifiable security credentials and transparent methodologies. Ensure they aren’t using black-hat techniques, but also that they’re keeping your data safe with strong data governance protocols.
Unvetted or inadequately secured tools can become a vehicle for data exfiltration, and these can expose campaign blueprints and keyword strategies, perhaps even selling them to competitors or malicious actors.
In the world of link building, it’s common to rely on platforms that act as intermediaries between publishers and site owners. Some, like LEOlytics, have introduced onboarding steps that help structure this connection more effectively. While each platform has its own approach, having some level of oversight can contribute to a more secure and organized process overall.
Another protocol to employ when working directly with freelance writers is to ask for an NDA signature, helping secure the confidence of your work. And, if sharing strategic or client information with AI assistants, be sure to turn on data privacy settings so your information isn’t used during their next model training.
SEO demands more thought on security than ever before. Because of its changing demands, many are turning to specialists and AI to help. But, in such unstable times, it’s more important than ever to remain vigilant about being blacklisted, using unsecure tools, or collaborating with unreliable agencies.
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10 Advanced SEO Techniques To Grow Your Site Traffic (2025) – Shopify
/in website SEO, Website Traffic/by Team ZYTStart selling with Shopify today
Start your free trial with Shopify today—then use these resources to guide you through every step of the process.
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Advanced SEO techniques, like building deep topic clusters and creating link magnets, can help you win more traffic from organic search.
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Basic SEO skills can take your online store far, but advanced techniques can dramatically boost your traffic and conversions. While fundamental SEO—like setting up Google Search Console and doing keyword research—improves search visibility, advanced strategies help you fine-tune performance for meaningful growth.
Here’s how to use advanced SEO techniques to optimize your site’s performance.
Advanced SEO refers to the tactics you use to improve your website’s search performance after implementing SEO fundamentals like setting up Google Search Console and Google Analytics, checking that your site is indexed, and doing your first tranche of keyword research. Advanced SEO focuses on fine-tuning your content strategy, keyword targeting, and site structure to maximize visibility in search results and drive more high-quality organic traffic.
Advanced SEO includes sophisticated keyword research, in-depth SEO content marketing strategy, targeted link-building, and technical site optimization. Here are 10 advanced SEO tactics to boost your ecommerce website’s performance:
Featured snippets are exactly what they sound like: short excerpts that succinctly answer a searcher’s question, which Google extracts from a top-ranking web page and displays at the top of the search engine results pages (SERPs). Earning this prime real estate boosts your site’s visibility and drives more clicks.
Use competitor keyword research to identify your competitors’ featured snippets. First, look at the content—snippets that provide general, vague, or inaccurate information or inadequately answer a user’s search intent. Then, create better content that targets the same keywords using SERP-friendly formats like definitions, tables, and lists to answer questions in less than 60 words.
Also examine the titles—look for instances where a snippet isn’t placed under a targeted heading. Then, target that snippet with your own optimized heading and content combination.
Google also places AI-generated overviews at the top of the SERP. Rather than excerpting one article to give a brief answer, they synthesize multiple top articles into a summary. Citations to the articles that informed the AI summary appear on the right. Appearing in these citations is the next frontier of SEO. Luckily, many strategists believe that visibility in citations follows the same core principles of traditional SEO.
Google prioritizes websites that go deep on specific topics over those that cover many topics superficially. Going deep means building out entire clusters of content that explore different aspects or subtopics of one big idea or theme.
Robust topic clusters position you as an authority on particular subjects and create internal linking opportunities—both of which boost SEO performance. Add relevant internal links to your existing content, and generate a list of topics for future content that build upon existing clusters or form new ones.
Advanced SEO strategists continuously monitor which pages rank successfully and which don’t, then look for improvement. Perform a content audit to identify high- and low-performing pages.
Study high performers for patterns, and implement those patterns across other pages. Focus on pages ranking in positions 5 to 9 on the SERP—these are within striking distance of high performance. Improving these pages could generate meaningful traffic increases. Higher positions typically earn several times more clicks than lower positions.
Also, identify pages getting zero traffic. You may want to consider unpublishing some or all of these—just make sure to set up redirects.
Metadata is information about your website that search engines can read via HTML, including title tags, meta descriptions, and alt text. Add relevant keywords to your metadata and update your title tags to reflect current trends to boost your performance in search results—but don’t misrepresent your content. For example, you can update “Top 10 Toaster Ovens in 2024” to “Top 10 Toaster Ovens in 2025”—but changing dates on outdated content without updating the actual information can damage brand trust.
Merchant listings help ecommerce businesses claim more SERP real estate areas, like Google Shopping. Rich snippets display additional data (such as review score or product price) on SERPs, targeting users ready to buy.
Merchant listings and rich snippets rely on structured data. Follow Google’s step-by-step guide to ecommerce schema markup to target product snippets, merchant listings, or both. Although elements use slightly different data schemes, Google notes that listing-focused data markup typically qualifies pages for product snippets, too.
Google and other search engines constantly adjust their search engine algorithms, SERP formats, and ranking factors. Stay up to date by reading SEO-focused blog content and articles in trade publications and blogs, like Exploding Topics and Search Engine Journal.
Link building improves your site’s authority, but earning quality backlinks requires strategic content creation. Create content that appeals to writers, journalists, and industry sites to earn citations in media coverage or on high-quality websites. Include original or proprietary quantitative data—this is what reputable websites link to when citing your content.
Here’s an example: Say your brand creates bedding using only natural fibers. You might publish a report featuring proprietary survey data that quantifies your customers’ preference for natural versus synthetic fibers. If your report says, “Our customers prefer natural fibers over synthetic options. Switching from poly blend to cotton-modal improved customer satisfaction and reduced return rates,” a journalist might write, “Some businesses report increased customer satisfaction after switching from synthetic materials to natural ones.”
But if your report says, “Our customers prefer natural fibers over synthetic options. Switching from poly-blend to cotton-modal reduced t-shirt return rates by 65% and increased customer satisfaction by 40% among t-shirt shoppers and 15% overall,” a journalist would write, “Many businesses find that customers prefer natural fibers. Ecommerce apparel brand Swagiography saw a 65% reduction in t-shirt return rates and a 40% increase in buyer satisfaction after switching to cotton-modal from poly blend.”
The first option doesn’t require citation—writers determined to substantiate a claim will look elsewhere for specific information. The second option meets this need, and most authoritative sites will include a backlink to your content to validate the source.
You can also monitor current events relevant to your industry to anticipate writer queries, or subscribe to industry mailing lists and offer expertise as a subject-matter expert. Both strategies can increase your odds of earning article citations.
You can use paid search engine advertising as an advanced link-building strategy. Here’s how it works:
1. Identify informational search terms relevant to your business or expertise, prioritizing keywords that require quantitative, historical, or expert-validated answers, such as “percentage of dogs with basic obedience training,” “dog training statistics,” and “dog not eating veterinarian advice.”
2. Run paid search ads targeting these keywords. Search ads appear above organic search results, helping writers, marketers, and journalists find your site.
Getting your site indexed by Google is basic SEO; it helps Google crawl your site and identify key pages. To level up, use a site crawler to spot problems like broken internal links, redirects, or slow page speed.
You can review crawl behavior in Google Search Console’s crawl data report or download your log files (which are auto-generated catalogs of your site’s usage history) and inspect them with a free tool like Screaming Frog Log File Analyser.
Internal link building encourages users to explore your site by sending them to other relevant content. It also improves search engine crawl efficiency by helping crawl bots navigate your site. Here are some best practices:
Link to important pages. Drive readers from blog posts, landing pages, or company news to the most important pages relevant to the referring page content. A blog post titled “How to fix water-damaged loafers” might link to your footwear category page, protective shoe wax, and leather care resource hub, for example.
Link from successful pages. You can also link from high-performing pages to key pages with lower authority scores or poorer search visibility. This strategy helps site users locate these pages and can improve their performance in search engine results.
Optimize anchor text. Anchor text is the word or string of words attached to a hyperlink. Choose concise, descriptive phrases relevant to both the host page and the search intent that the linked page targets. Consider the sentence, “Waterproof leather sealant helps your feet stay dry when the sidewalks aren’t.” “Waterproof leather sealant” is the best anchor text for a company that sells waterproof leather sealant, and “helps your feet stay dry” is the right choice for a brand that sells rain boots.
Advanced SEO focuses on fine-tuning content strategy, keyword targeting, and site structure to maximize search visibility and drive more high-quality organic traffic. It requires more technical and content marketing expertise than basic SEO and can increase the precision and flexibility of your SEO strategy.
Yes. Search generates 68% of all web traffic, and first-page Google results earn 27.6% of clicks for relevant searches. This makes a basic SEO strategy essential for promoting your brand online.
Many ecommerce business owners manage basic SEO for their own sites, and you can self-teach or take online SEO courses to learn more advanced techniques. Choose a website builder with built-in SEO tools and consider consulting an SEO and content strategy expert for guidance.
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