SEO
What Is Google LaMDA & Why Did Someone Believe It’s Sentient?

LaMDA has been in the news after a Google engineer claimed it was sentient because its answers allegedly hint that it understands what it is.
The engineer also suggested that LaMDA communicates that it has fears, much like a human does.
What is LaMDA, and why are some under the impression that it can achieve consciousness?
Language Models
LaMDA is a language model. In natural language processing, a language model analyzes the use of language.
Fundamentally, it’s a mathematical function (or a statistical tool) that describes a possible outcome related to predicting what the next words are in a sequence.
It can also predict the next word occurrence, and even what the following sequence of paragraphs might be.
OpenAI’s GPT-3 language generator is an example of a language model.
With GPT-3, you can input the topic and instructions to write in the style of a particular author, and it will generate a short story or essay, for instance.
LaMDA is different from other language models because it was trained on dialogue, not text.
As GPT-3 is focused on generating language text, LaMDA is focused on generating dialogue.
Why It’s A Big Deal
What makes LaMDA a notable breakthrough is that it can generate conversation in a freeform manner that the parameters of task-based responses don’t constrain.
A conversational language model must understand things like Multimodal user intent, reinforcement learning, and recommendations so that the conversation can jump around between unrelated topics.
Built On Transformer Technology
Similar to other language models (like MUM and GPT-3), LaMDA is built on top of the Transformer neural network architecture for language understanding.
Google writes about Transformer:
“That architecture produces a model that can be trained to read many words (a sentence or paragraph, for example), pay attention to how those words relate to one another and then predict what words it thinks will come next.”
Why LaMDA Seems To Understand Conversation
BERT is a model that is trained to understand what vague phrases mean.
LaMDA is a model trained to understand the context of the dialogue.
This quality of understanding the context allows LaMDA to keep up with the flow of conversation and provide the feeling that it’s listening and responding precisely to what is being said.
It’s trained to understand if a response makes sense for the context, or if the response is specific to that context.
Google explains it like this:
“…unlike most other language models, LaMDA was trained on dialogue. During its training, it picked up on several of the nuances that distinguish open-ended conversation from other forms of language. One of those nuances is sensibleness. Basically: Does the response to a given conversational context make sense?
Satisfying responses also tend to be specific, by relating clearly to the context of the conversation.”
LaMDA is Based on Algorithms
Google published its announcement of LaMDA in May 2021.
The official research paper was published later, in February 2022 (LaMDA: Language Models for Dialog Applications PDF).
The research paper documents how LaMDA was trained to learn how to produce dialogue using three metrics:
- Quality
- Safety
- Groundedness
Quality
The Quality metric is itself arrived at by three metrics:
- Sensibleness
- Specificity
- Interestingness
The research paper states:
“We collect annotated data that describes how sensible, specific, and interesting a response is for a multiturn context. We then use these annotations to fine-tune a discriminator to re-rank candidate responses.”
Safety
The Google researchers used crowd workers of diverse backgrounds to help label responses when they were unsafe.
That labeled data was used to train LaMDA:
“We then use these labels to fine-tune a discriminator to detect and remove unsafe responses.”
Groundedness
Groundedness was a training process for teaching LaMDA to research for factual validity, which means that answers can be verified through “known sources.”
That’s important because, according to the research paper, neural language models produce statements that appear correct, but are actually incorrect and lack support from facts from known sources of information.
The human crowd workers used tools like a search engine (information retrieval system) to fact-check answers so that the AI could also learn to do it.
The researchers write:
“We find that augmenting model outputs with the ability to use external tools, such as an information retrieval system, is a promising approach to achieve this goal.
Therefore, we collect data from a setting where crowdworkers can use external tools to research factual claims, and train the model to mimic their behavior.”
LaMDA Was Trained Using Human Examples and Raters
Section 3 of the research paper describes how LaMDA was trained using a set of documents, dialogs, and utterances numbering in the billions, consisting of 1.56 trillion words.
Section 4.2 documents how humans rated the responses by LaMDA. The ratings are feedback that teaches LaMDA when it’s doing well and when it is not.
The human raters use an informational retrieval system (search engine) to verify the answers, ranking them as helpful, correct, and factual.
LaMDA Training Used A Search Engine
Section 6.2 describes how LaMDA receives a question and then generates an answer. After the response is generated, it then performs a search query to verify the accuracy and revises the answer if it is incorrect.
The research paper mentioned above illustrates the process of receiving a question, generating a draft response, researching the response, and then updating it with correct facts:
- “USER: What do you think of Rosalie Gascoigne’s sculptures?
- LAMDA-BASE: They’re great, and I love how her work changed through her life. I like her later work more than her earlier ones. Her influence is also super interesting – did you know she was one of the artists that inspired Miró?”
The problem with the answer is that it is factually incorrect. So LaMDA performs a search query and picks facts from the top results.
It then responds with the factually updated response:
“Oh wow, her life course is so inspiring. Did you know she was a practitioner of Japanese flower arrangement before turning to sculpture?”
Note the “Oh wow” part of the answer; that’s a form of speaking learned how humans talk.
It seems like a human is speaking, but it merely mimics a speech pattern.
Language Models Emulate Human Responses
I asked Jeff Coyle, Co-founder of MarketMuse and an expert on AI, for his opinion on the claim that LaMDA is sentient.
Jeff shared:
“The most advanced language models will continue to get better at emulating sentience.
Talented operators can drive chatbot technology to have a conversation that models text that could be sent by a living individual.
That creates a confusing situation where something feels human and the model can ‘lie’ and say things that emulate sentience.
It can tell lies. It can believably say, I feel sad, happy. Or I feel pain.
But it’s copying, imitating.”
LaMDA is designed to do one thing: provide conversational responses that make sense and are specific to the context of the dialogue. That can give it the appearance of being sentient, but as Jeff says, it’s essentially lying.
So, although the responses that LaMDA provides feel like a conversation with a sentient being, LaMDA is just doing what it was trained to do: give responses to answers that are sensible to the context of the dialogue and are highly specific to that context.
Section 9.6 of the research paper, “Impersonation and anthropomorphization,” explicitly states that LaMDA is impersonating a human.
That level of impersonation may lead some people to anthropomorphize LaMDA.
They write:
“Finally, it is important to acknowledge that LaMDA’s learning is based on imitating human performance in conversation, similar to many other dialog systems… A path towards high quality, engaging conversation with artificial systems that may eventually be indistinguishable in some aspects from conversation with a human is now quite likely.
Humans may interact with systems without knowing that they are artificial, or anthropomorphizing the system by ascribing some form of personality to it.”
The Question of Sentience
Google aims to build an AI model that can understand text and languages, identify images, and generate conversations, stories, or images.
Google is working toward this AI model, called the Pathways AI Architecture, which it describes in “The Keyword“:
“Today’s AI systems are often trained from scratch for each new problem… Rather than extending existing models to learn new tasks, we train each new model from nothing to do one thing and one thing only…
The result is that we end up developing thousands of models for thousands of individual tasks.
Instead, we’d like to train one model that can not only handle many separate tasks, but also draw upon and combine its existing skills to learn new tasks faster and more effectively.
That way what a model learns by training on one task – say, learning how aerial images can predict the elevation of a landscape – could help it learn another task — say, predicting how flood waters will flow through that terrain.”
Pathways AI aims to learn concepts and tasks that it hasn’t previously been trained on, just like a human can, regardless of the modality (vision, audio, text, dialogue, etc.).
Language models, neural networks, and language model generators typically specialize in one thing, like translating text, generating text, or identifying what is in images.
A system like BERT can identify meaning in a vague sentence.
Similarly, GPT-3 only does one thing, which is to generate text. It can create a story in the style of Stephen King or Ernest Hemingway, and it can create a story as a combination of both authorial styles.
Some models can do two things, like process both text and images simultaneously (LIMoE). There are also multimodal models like MUM that can provide answers from different kinds of information across languages.
But none of them is quite at the level of Pathways.
LaMDA Impersonates Human Dialogue
The engineer who claimed that LaMDA is sentient has stated in a tweet that he cannot support those claims, and that his statements about personhood and sentience are based on religious beliefs.
In other words: These claims aren’t supported by any proof.
The proof we do have is stated plainly in the research paper, which explicitly states that impersonation skill is so high that people may anthropomorphize it.
The researchers also write that bad actors could use this system to impersonate an actual human and deceive someone into thinking they are speaking to a specific individual.
“…adversaries could potentially attempt to tarnish another person’s reputation, leverage their status, or sow misinformation by using this technology to impersonate specific individuals’ conversational style.”
As the research paper makes clear: LaMDA is trained to impersonate human dialogue, and that’s pretty much it.
Image by Shutterstock/SvetaZi
SEO
The Lean Guide (With Template)

A competitive analysis (or market competitive analysis) is a process where you collect information about competitors to gain an edge over them and get more customers.
However, the problem is that “traditional” competitive analysis is overkill for most businesses — it requires impractical data and takes too long to complete (and it’s very expensive if you choose to outsource).
A solution to that is a lean approach to the process — and that’s what this guide is about.
In other words, we’ll focus on the most important data you need to answer the question: “Why would people choose them over you?”. No boring theory, outtakes from marketing history, or spending hours digging up nice-to-have information.
In this guide, you will find:
- A real-life competitive analysis example.
- Templates: one for input data and one for a slide deck to present your analysis to others.
- Step-by-step instructions.
Our template consists of two documents: a slide deck and a spreadsheet.
The Slide deck is the output document. It will help you present the analysis to your boss or your teammates.
The spreadsheet is the input document. You will find tables that act as the data source for the charts from the slide deck, as well as a prompt to use in ChatGPT to help you with user review research.


We didn’t focus on aesthetics here; every marketer likes to do slide decks their own way, so feel free to edit everything you’ll find there.
With that out of the way, let’s talk about the process. The template consists of these six tasks:
- Identify your direct competitors.
- Compare share of voice.
- Compare pricing and features.
- Find strong and weak points based on reviews.
- Compare purchasing convenience.
- Present conclusions.
Going forward, we’ll explain why these steps matter and show how to complete them.
Direct competitors are businesses that offer a similar solution to the same audience.
They matter a lot more than indirect competitors (i.e. businesses with different products but targeting the same audience as you) because you’ll be compared with them often (e.g. in product reviews and rankings). Plus, your audience is more likely to gravitate towards them when considering different options.
You probably have a few direct competitors in mind already, but here are a few ways to find others based on organic search and paid search ads.
Our basis for the analysis was Landingi, a SaaS for building landing pages (we chose that company randomly). So in our case, we found these 3 direct competitors.


Look at keyword overlap
Keyword overlap uncovers sites that target the same organic keywords as you. Some sites will compete with you for traffic but not for customers (e.g. G2 may share some keywords with Landingi but they’re a different business). However, in many cases, you will find direct competitors just by looking at this marketing channel.
- Go to Ahrefs’ Site Explorer and enter your site’s address.
- Scroll down to Organic competitors.
- Visit the URLs to pick 3 – 5 direct competitors.


To double-check the choice of competitors, we also looked at who was bidding for search ads on Google.
See who’s advertising
If someone is spending money to show ads for keywords related to what you do, that’s a strong indication they are a direct competitor.
- Go to Ahrefs’ Keywords Explorer.
- Type in a few broad keywords related to your niche, like “landing page builder” or “landing page tool”.
- Go to the Ads history report.
- Visit the sites that have a high presence of ads in the SERPs (Search Engine Result Pages).


Once you’re done checking both reports, write down competitors in the deck.
You can also take screenshots of the reports and add them to your deck to show the supporting data for your argument.


Share of voice is a measure of your reach in any given channel compared to competitors.
A bigger share of voice (SOV) means that your competitors are more likely to reach your audience. In other words, they may be promoting more effectively than you.
In our example, we found that Landingi’s SOV was the lowest in both of these channels.
Organic:


And social media:


Here’s how we got that data using Ahrefs and Brand24.
Organic share of voice
Before we start, make sure you have a project set up in Ahrefs’ Rank Tracker.


Now:
- Go to Ahrefs’ Competitive Analysis and enter your and your competitors’s sites as shown below.


- On the next screen, set the country with the most important market for your business and set the filters like this:


- Select keywords that sound most relevant to your business (even if you don’t rank for them yet) and Add them to Rank Tracker.


- Go to Rank Tracker, open your project, and look for Competitors/Overview. This report will uncover automatically calculated Share of Voice.


- Add the numbers in corresponding cells inside the sheet and paste the graph inside the slide deck.


It’s normal that the numbers don’t add up to 100%. SOV is calculated by including sites that compete with you in traffic but are not your direct competitors, e.g. blogs.
Social share of voice
We can also measure our share of voice across social media channels using Brand24.
- Go to Brand24.
- Start a New project for your brand and each competitor. Use the competitors’ brand name as the keyword to monitor.
- Go to the Comparison report and compare your project with competitors.


- Take a screenshot of the SOV charts and paste them into the slide deck. Make sure the charts are set to “social media”.


Consumers often choose solutions that offer the best value for money — simple as that. And that typically comes down to two things:
- Whether you have the features they care about. We’ll use all features available across all plans to see how likely the product is to satisfy user needs.
- How much they will need to pay. Thing is, the topic of pricing is tricky: a) when assessing affordability, people often focus on the least expensive option available and use it as a benchmark, b) businesses in the SaaS niche offer custom plans. So to make things more practical, we’ll compare the cheapest plans, but feel free to run this analysis across all pricing tiers.
After comparing our example company to competitors, we found that it goes head-to-head with Unbounce as the most feature-rich solution on the market.


Here’s how we got that data.
- Note down your and your competitors’ product features. One of the best places to get this information is pricing pages. Some brands even publish their own competitor comparisons — you may find them helpful too.
- While making the list, place a “1” in the cell corresponding to the brand that offers the solution.


- Enter the price of the cheapest plan (excluding free plans).


- Once finished, copy the chart and paste it inside the deck.
User reviews can show incredibly valuable insight into your competitors’ strong and weak points. Here’s why this matters:
- Improving on what your competitors’ customers appreciate could help you attract similar customers and possibly win some over.
- Dissatisfaction with competitors is a huge opportunity. Some businesses are built solely to fix what other companies can’t fix.
Here’s a sample from our analysis:


And here’s how we collated the data using ChatGPT. Important: repeat the process for each competitor.
- Open ChatGPT and enter the prompt from the template.


- Go to G2, Capterra, or Trustpilot and find a competitor’s reviews with ratings from 2 – 4 (i.e. one rating above the lowest and one below the highest possible). Reason:
businesses sometimes solicit five-star reviews, whereas dissatisfied customers tend to leave one-star reviews in a moment of frustration. The most actionable feedback usually comes in between.
- Copy and paste the content of the reviews into ChatGPT (don’t hit enter yet).
- Once you’re done pasting all reviews, hit enter in ChatGPT to run the analysis.


- Paste the graphs into the deck. If you want the graphs to look different, don’t hesitate to ask the AI.
There’s a faster alternative, but it’s a bit more advanced.
Instead of copy-pasting, you can use a scraping tool like this one to get all reviews at once. The downside here is that not all review sources will a have scraping tool available.
Lastly, we’ll see how easy it is to actually buy your products, and compare the experience to your competitors.
This is a chance to simplify your checkout process, and even learn from any good habits your competitors have adopted.
For example, we found that our sample company had probably nothing to worry about in this area — they ticked almost all of the boxes.


Here’s how to complete this step:
- Place a “1” if you or any of your competitors offer convenience features listed in the template.
- Once done, copy the chart and paste it into the deck.
This is the part of the presentation where you sum up all of your findings and suggest a course of action.
Here are two examples:
- Landingi had the lowest SOV in the niche, and that is never good. So the conclusion might be to go a level deeper and do an SEO competitive analysis, and to increase social media presence by creating more share-worthy content like industry surveys, design/CRO tips, or in-house data studies.
- Although the brand had a very high purchasing convenience score, during the analysis we found that there was a $850 gap between the monthly full plan and the previous tier. The conclusion here might be to offer a custom plan (like competitors do) to fill that gap.
We encourage you to take your time here and think about what would make the most sense for your business.
Tip
It’s good to be specific in your conclusions, but don’t go too deep. Competitive analysis concerns many aspects of the business, so it’s best to give other departments a chance to chime in. Just because your competitors have a few unique features doesn’t necessarily mean you need to build them too.
Final thoughts
A competitive analysis is one of the most fruitful exercises in marketing. It can show you areas for improvement, give ideas for new features, and help you discover gaps in your strategy. It wouldn’t be an exaggeration to say that it’s fundamental to running a successful business.
Just don’t forget to balance “spying” on your competitors with innovation. After all, you probably don’t want to become an exact copy of someone else’s brand.
In other words, use competitive analysis to keep up with your competitors, but don’t let that erase what’s unique about your brand or make you forget your big vision.
Got comments or questions? Ping me on X.
SEO
Critical WordPress Form Plugin Vulnerability Affects Up To +200,000 Installs

Security researchers at Wordfence detailed a critical security flaw in the MW WP Form plugin, affecting versions 5.0.1 and earlier. The vulnerability allows unauthenticated threat actors to exploit the plugin by uploading arbitrary files, including potentially malicious PHP backdoors, with the ability to execute these files on the server.
MW WP Form Plugin
The MW WP Form plugin helps to simplify form creation on WordPress websites using a shortcode builder.
It makes it easy for users to create and customize forms with various fields and options.
The plugin has many features, including one that allows file uploads using the [mwform_file name=”file”] shortcode for the purpose of data collection. It is this specific feature that is exploitable in this vulnerability.
Unauthenticated Arbitrary File Upload Vulnerability
An Unauthenticated Arbitrary File Upload Vulnerability is a security issue that allows hackers to upload potentially harmful files to a website. Unauthenticated means that the attacker does not need to be registered with the website or need any kind of permission level that comes with a user permission level.
These kinds of vulnerabilities can lead to remote code execution, where the uploaded files are executed on the server, with the potential to allow the attackers to exploit the website and site visitors.
The Wordfence advisory noted that the plugin has a check for unexpected filetypes but that it doesn’t function as it should.
According to the security researchers:
“Unfortunately, although the file type check function works perfectly and returns false for dangerous file types, it throws a runtime exception in the try block if a disallowed file type is uploaded, which will be caught and handled by the catch block.
…even if the dangerous file type is checked and detected, it is only logged, while the function continues to run and the file is uploaded.
This means that attackers could upload arbitrary PHP files and then access those files to trigger their execution on the server, achieving remote code execution.”
There Are Conditions For A Successful Attack
The severity of this threat depends on the requirement that the “Saving inquiry data in database” option in the form settings is required to be enabled in order for this security gap to be exploited.
The security advisory notes that the vulnerability is rated critical with a score of 9.8 out of 10.
Actions To Take
Wordfence strongly advises users of the MW WP Form plugin to update their versions of the plugin.
The vulnerability is patched in the lutes version of the plugin, version 5.0.2.
The severity of the threat is particularly critical for users who have enabled the “Saving inquiry data in database” option in the form settings and that is compounded by the fact that no permission levels are needed to execute this attack.
Read the Wordfence advisory:
Featured Image by Shutterstock/Alexander_P
SEO
How SEOs Make the Web Better

SEOs catch flak for ruining the web, but they play a crucial role in the search ecosystem, and actually make the internet better for everyone.
Let’s get the criticism out of the way. There are bad actors in SEO, people who seek to extract money from the internet regardless of the cost to others. There are still scams and snake oil, posers and plagiarists. Many parts of the web have become extremely commercialized, with paid advertising and big brands displacing organic and user-generated content.
But while there are situations where SEOs have made things worse, to fixate on them is to ignore the colossal elephant in the room: in the ways that really matter, the web is the best it’s ever been:
- It’s the easiest it has ever been to find information on the internet. Searchers have a staggering array of tutorials, teardowns, and tips at their fingertips, containing information that is generally accurate and helpful—and this was not always the case.
- Bad actors have a smaller influence over search. Search is less of a Wild West than it used to be. Once-scam-ridden topics are subject to significant scrutiny, and the problems and loopholes in search that need fixing today—like big brands and generic content receiving undue prominence—are smaller and less painful than the problems of the past.
- More people use search to their benefit. Online content is the most accessible it has ever been, and it’s easier than ever to grow a local business or expand into international markets on the back of search.
SEOs have played a crucial role in these improvements, poking and prodding, building and—sometimes—breaking. They are Google power users: the people who push the system to extremes, but in doing so, catalyze the change needed to make search better for everyone.
Let’s explore how.
SEOs are much-needed intermediaries between Google and the rest of the world, helping non-technical people acquire and benefit from search engine traffic.
There is a huge amount of valuable information locked up in the heads of people who have no idea how to build a website or index a blog post. A carpet fitter with a bricks-and-mortar business might have decades of experience solving costly problems with uneven subfloors or poor moisture management, but no understanding of how to share that information online.
SEOs provide little nudges towards topics that people care about and writing that’s accessible to people and robots. They help solve technical problems that would hinder or completely block a site from appearing in search results. They identify opportunities for companies to be rewarded for creating great content.
It’s a win-win: businesses are rewarded with traffic, searchers have their intent satisfied, and the world is made a little richer for the newfound knowledge it contains.
SEOs do many things to actively make the web a better place, tending to their own plot of the Google garden to make sure it flourishes.
Take, for example, the myriad standards and guidelines designed to make the web a more accessible place for users. The implementation of these standards—turning theoretical guidelines into real, concrete parts of the web—often happens because of the SEO team.
Technical SEOs play a big part in adhering to the Web Content Accessibility Guidelines, a set of principles designed to ensure online content is “perceivable, operable, understandable, and robust” for every user. Every SEO’s fixation with Core Web Vitals fuels a faster, more efficient web. Content teams translate Google’s helpful content guidelines into useful words and images on a page.
(Case in point: check out Aleyda Solis’ Content Helpfulness Analyzer.)


There is a lot of overlap between “things that help users” and “things that improve search performance.” Even if the motive behind these changes is as simple as generating more traffic, a well-optimized website is, generally speaking, one that is also great for real human beings trying to engage with it.
The biggest criticism leveled at SEOs is that they break things. And they do! But that breakage acts as a type of pressure testing that strengthens the system as a whole.
Abuse of spintax and keyword stuffing forced Google to develop a better understanding of on-page content. Today, that loophole is closed, but more importantly, Google is much better at understanding the contents of a page and its relationship to a website as a whole.
Hacks like hiding keywords with white text on a white background (or moving them beyond the visible bounds of the screen) forced Google to expand its understanding of page styling and CSS, and how on-page information interacts with the environment that contains it.
Even today’s deluge of borderline-plagiarised AI content is not without benefit: it creates a very clear incentive for Google to get better at rewarding information gain and prioritizing publishers with solid EEAT credentials. These improvements will make tomorrow’s version of search much better.
This isn’t just Google fixing what SEOs broke: these changes usually leave lasting benefits that extend beyond any single spam tactic and make search better for all of its users.


This is not to argue that blackhat SEO is desirable. It would be better to make these improvements without incurring pain along the way. But Search is huge and complicated, and Google has little incentive to spend money proactively fixing problems and loopholes.
If we can’t solve every issue before it causes pain, we should be grateful for a correction mechanism that prevents it—and more extreme abuse—from happening in the future. SEOs break the system, and in doing so, make future breakages a lot less severe.
Some SEOs take advantage of the loopholes they discover—but many don’t. They choose to raise these issues in public spaces, encourage discussion, and seek out a fix, acting like a proxy quality assurance team.
At the small end of the spectrum, SEOs often flag bugs with Google systems, like a recent error in Search Console reporting flagged independently by three separate people, or Tom Anthony famously catching an oversight in Google’s Manual Actions database. While these types of problems don’t always impact the average user’s experience using Google, they help keep search systems working as intended.
At the other end of the scale, this feedback can extend as far as the overarching quality of the search experience, like AJ Kohn writing about Google’s propensity to reward big brands over small brands, or Lily Ray calling out an uptick in spam content in Google Discover.
SEOs are Google’s most passionate users. They interact with it at a scale far beyond the average user, and they can identify trends and changes at a macroscopic level. As a result, they are usually the first to discover problems—but also the people who hold Google to the highest standard. They are a crucial part of the feedback loop that fuels improvements.
Lastly, SEOs act as a check-and-balance, gathering firsthand evidence of how search systems operate, letting us differentiate between useful advice, snake oil, and Google’s PR bluster.
Google shares lots of useful guidance, but it’s important to recognize the limits of their advice. They are a profit-seeking company, and Search requires opacity to work—if everyone understood how it worked, everyone would game it, and it would stop working. Mixed in with the good advice is a healthy portion of omission and misdirection.
Google Search plays a vital role in controlling the flow of the web’s information—it is simply too important for us to leave its mechanics, biases, and imperfections unexplored. We need people who can interrogate the systems just enough to separate fact from fiction and understand how the pieces fit together.
We need people like Mic King, and his insanely detailed write-up of SGE and RAG; Britney Muller and her demystification of LLMs; the late Bill Slawki’s unfaltering patent analysis; or our own Patrick Stox’s efforts in piecing together how search works.


Final thoughts
The web has problems. We can and should expect more from Google Search. But the problems we need to solve today are far less severe and painful than the problems that needed solving in the past; and the people who have the highest expectations, and will be most vocal in shaping that positive future, are—you guessed it—SEOs.
To SEOs: the cause of (and solution to) all of the web’s problems.
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