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What Is It & How Can You Use It?

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What Is It & How Can You Use It?

OpenAI introduced a long-form question-answering AI called ChatGPT that answers complex questions conversationally.

It’s a revolutionary technology because it’s trained to learn what humans mean when they ask a question.

Many users are awed at its ability to provide human-quality responses, inspiring the feeling that it may eventually have the power to disrupt how humans interact with computers and change how information is retrieved.

What Is ChatGPT?

ChatGPT is a large language model chatbot developed by OpenAI based on GPT-3.5. It has a remarkable ability to interact in conversational dialogue form and provide responses that can appear surprisingly human.

Large language models perform the task of predicting the next word in a series of words.

Reinforcement Learning with Human Feedback (RLHF) is an additional layer of training that uses human feedback to help ChatGPT learn the ability to follow directions and generate responses that are satisfactory to humans.

Who Built ChatGPT?

ChatGPT was created by San Francisco-based artificial intelligence company OpenAI. OpenAI Inc. is the non-profit parent company of the for-profit OpenAI LP.

OpenAI is famous for its well-known DALL·E, a deep-learning model that generates images from text instructions called prompts.

The CEO is Sam Altman, who previously was president of Y Combinator.

Microsoft is a partner and investor in the amount of $1 billion dollars. They jointly developed the Azure AI Platform.

Large Language Models

ChatGPT is a large language model (LLM). Large Language Models (LLMs) are trained with massive amounts of data to accurately predict what word comes next in a sentence.

It was discovered that increasing the amount of data increased the ability of the language models to do more.

According to Stanford University:

“GPT-3 has 175 billion parameters and was trained on 570 gigabytes of text. For comparison, its predecessor, GPT-2, was over 100 times smaller at 1.5 billion parameters.

This increase in scale drastically changes the behavior of the model — GPT-3 is able to perform tasks it was not explicitly trained on, like translating sentences from English to French, with few to no training examples.

This behavior was mostly absent in GPT-2. Furthermore, for some tasks, GPT-3 outperforms models that were explicitly trained to solve those tasks, although in other tasks it falls short.”

LLMs predict the next word in a series of words in a sentence and the next sentences – kind of like autocomplete, but at a mind-bending scale.

This ability allows them to write paragraphs and entire pages of content.

But LLMs are limited in that they don’t always understand exactly what a human wants.

And that’s where ChatGPT improves on state of the art, with the aforementioned Reinforcement Learning with Human Feedback (RLHF) training.

How Was ChatGPT Trained?

GPT-3.5 was trained on massive amounts of data about code and information from the internet, including sources like Reddit discussions, to help ChatGPT learn dialogue and attain a human style of responding.

ChatGPT was also trained using human feedback (a technique called Reinforcement Learning with Human Feedback) so that the AI learned what humans expected when they asked a question. Training the LLM this way is revolutionary because it goes beyond simply training the LLM to predict the next word.

A March 2022 research paper titled Training Language Models to Follow Instructions with Human Feedback explains why this is a breakthrough approach:

“This work is motivated by our aim to increase the positive impact of large language models by training them to do what a given set of humans want them to do.

By default, language models optimize the next word prediction objective, which is only a proxy for what we want these models to do.

Our results indicate that our techniques hold promise for making language models more helpful, truthful, and harmless.

Making language models bigger does not inherently make them better at following a user’s intent.

For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user.

In other words, these models are not aligned with their users.”

The engineers who built ChatGPT hired contractors (called labelers) to rate the outputs of the two systems, GPT-3 and the new InstructGPT (a “sibling model” of ChatGPT).

Based on the ratings, the researchers came to the following conclusions:

“Labelers significantly prefer InstructGPT outputs over outputs from GPT-3.

InstructGPT models show improvements in truthfulness over GPT-3.

InstructGPT shows small improvements in toxicity over GPT-3, but not bias.”

The research paper concludes that the results for InstructGPT were positive. Still, it also noted that there was room for improvement.

“Overall, our results indicate that fine-tuning large language models using human preferences significantly improves their behavior on a wide range of tasks, though much work remains to be done to improve their safety and reliability.”

What sets ChatGPT apart from a simple chatbot is that it was specifically trained to understand the human intent in a question and provide helpful, truthful, and harmless answers.

Because of that training, ChatGPT may challenge certain questions and discard parts of the question that don’t make sense.

Another research paper related to ChatGPT shows how they trained the AI to predict what humans preferred.

The researchers noticed that the metrics used to rate the outputs of natural language processing AI resulted in machines that scored well on the metrics, but didn’t align with what humans expected.

The following is how the researchers explained the problem:

“Many machine learning applications optimize simple metrics which are only rough proxies for what the designer intends. This can lead to problems, such as YouTube recommendations promoting click-bait.”

So the solution they designed was to create an AI that could output answers optimized to what humans preferred.

To do that, they trained the AI using datasets of human comparisons between different answers so that the machine became better at predicting what humans judged to be satisfactory answers.

The paper shares that training was done by summarizing Reddit posts and also tested on summarizing news.

The research paper from February 2022 is called Learning to Summarize from Human Feedback.

The researchers write:

“In this work, we show that it is possible to significantly improve summary quality by training a model to optimize for human preferences.

We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning.”

What are the Limitations of ChatGTP?

Limitations on Toxic Response

ChatGPT is specifically programmed not to provide toxic or harmful responses. So it will avoid answering those kinds of questions.

Quality of Answers Depends on Quality of Directions

An important limitation of ChatGPT is that the quality of the output depends on the quality of the input. In other words, expert directions (prompts) generate better answers.

Answers Are Not Always Correct

Another limitation is that because it is trained to provide answers that feel right to humans, the answers can trick humans that the output is correct.

Many users discovered that ChatGPT can provide incorrect answers, including some that are wildly incorrect.

The moderators at the coding Q&A website Stack Overflow may have discovered an unintended consequence of answers that feel right to humans.

Stack Overflow was flooded with user responses generated from ChatGPT that appeared to be correct, but a great many were wrong answers.

The thousands of answers overwhelmed the volunteer moderator team, prompting the administrators to enact a ban against any users who post answers generated from ChatGPT.

The flood of ChatGPT answers resulted in a post entitled: Temporary policy: ChatGPT is banned:

“This is a temporary policy intended to slow down the influx of answers and other content created with ChatGPT.

…The primary problem is that while the answers which ChatGPT produces have a high rate of being incorrect, they typically “look like” they “might” be good…”

The experience of Stack Overflow moderators with wrong ChatGPT answers that look right is something that OpenAI, the makers of ChatGPT, are aware of and warned about in their announcement of the new technology.

OpenAI Explains Limitations of ChatGPT

The OpenAI announcement offered this caveat:

“ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers.

Fixing this issue is challenging, as:

(1) during RL training, there’s currently no source of truth;

(2) training the model to be more cautious causes it to decline questions that it can answer correctly; and

(3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.”

Is ChatGPT Free To Use?

The use of ChatGPT is currently free during the “research preview” time.

The chatbot is currently open for users to try out and provide feedback on the responses so that the AI can become better at answering questions and to learn from its mistakes.

The official announcement states that OpenAI is eager to receive feedback about the mistakes:

“While we’ve made efforts to make the model refuse inappropriate requests, it will sometimes respond to harmful instructions or exhibit biased behavior.

We’re using the Moderation API to warn or block certain types of unsafe content, but we expect it to have some false negatives and positives for now.

We’re eager to collect user feedback to aid our ongoing work to improve this system.”

There is currently a contest with a prize of $500 in ChatGPT credits to encourage the public to rate the responses.

“Users are encouraged to provide feedback on problematic model outputs through the UI, as well as on false positives/negatives from the external content filter which is also part of the interface.

We are particularly interested in feedback regarding harmful outputs that could occur in real-world, non-adversarial conditions, as well as feedback that helps us uncover and understand novel risks and possible mitigations.

You can choose to enter the ChatGPT Feedback Contest3 for a chance to win up to $500 in API credits.

Entries can be submitted via the feedback form that is linked in the ChatGPT interface.”

The currently ongoing contest ends at 11:59 p.m. PST on December 31, 2022.

Will Language Models Replace Google Search?

Google itself has already created an AI chatbot that is called LaMDA. The performance of Google’s chatbot was so close to a human conversation that a Google engineer claimed that LaMDA was sentient.

Given how these large language models can answer so many questions, is it far-fetched that a company like OpenAI, Google, or Microsoft would one day replace traditional search with an AI chatbot?

Some on Twitter are already declaring that ChatGPT will be the next Google.

The scenario that a question-and-answer chatbot may one day replace Google is frightening to those who make a living as search marketing professionals.

It has sparked discussions in online search marketing communities, like the popular Facebook SEOSignals Lab where someone asked if searches might move away from search engines and towards chatbots.

Having tested ChatGPT, I have to agree that the fear of search being replaced with a chatbot is not unfounded.

The technology still has a long way to go, but it’s possible to envision a hybrid search and chatbot future for search.

But the current implementation of ChatGPT seems to be a tool that, at some point, will require the purchase of credits to use.

How Can ChatGPT Be Used?

ChatGPT can write code, poems, songs, and even short stories in the style of a specific author.

The expertise in following directions elevates ChatGPT from an information source to a tool that can be asked to accomplish a task.

This makes it useful for writing an essay on virtually any topic.

ChatGPT can function as a tool for generating outlines for articles or even entire novels.

It will provide a response for virtually any task that can be answered with written text.

Conclusion

As previously mentioned, ChatGPT is envisioned as a tool that the public will eventually have to pay to use.

Over a million users have registered to use ChatGPT within the first five days since it was opened to the public.

More resources:


Featured image: Shutterstock/Asier Romero

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The Lean Guide (With Template)

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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.

Competitive analysis template — spreadsheet sneak peek.Competitive analysis template — spreadsheet sneak peek.

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: 

  1. Identify your direct competitors. 
  2. Compare share of voice. 
  3. Compare pricing and features.
  4. Find strong and weak points based on reviews.
  5. Compare purchasing convenience.
  6. Present conclusions.

Going forward, we’ll explain why these steps matter and show how to complete them. 

1. Identify your direct competitors

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. 

Slide 1 — direct competitors.Slide 1 — 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.
Top organic competitors data from Ahrefs.Top organic competitors data from Ahrefs.

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). 
Ads history report in Ahrefs' Keywords Explorer.Ads history report in Ahrefs' Keywords Explorer.

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. 

 Slide 2 — direct competitors by organic traffic. Slide 2 — direct competitors by organic traffic.

2. Compare share of voice

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: 

Slide 3 — share of voice on Google Search.Slide 3 — share of voice on Google Search.

And social media:

 Slide 4 — share of voice on social media. Slide 4 — share of voice on 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

Create a new project in Ahrefs' Rank Tracker.Create a new project in Ahrefs' Rank Tracker.

Now: 

  • Go to Ahrefs’ Competitive Analysis and enter your and your competitors’s sites as shown below. 
Create a new project in Ahrefs' Rank Tracker.
Create a new project in Ahrefs' Rank Tracker.
  • On the next screen, set the country with the most important market for your business and set the filters like this:
Content gap analysis filter setup.Content gap analysis filter setup.
  • Select keywords that sound most relevant to your business (even if you don’t rank for them yet) and Add them to Rank Tracker
Common keywords found via Ahrefs' Competitive Analysis.Common keywords found via Ahrefs' Competitive Analysis.
  • Go to Rank Tracker, open your project, and look for Competitors/Overview. This report will uncover automatically calculated Share of Voice
Organic share of voice data in Ahrefs.Organic share of voice data in Ahrefs.
  • Add the numbers in corresponding cells inside the sheet and paste the graph inside the slide deck. 
Filling the share of voice template with data.Filling the share of voice template with data.

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. 
Using Brand24's Comparison tool for competitive analysis.Using Brand24's Comparison tool for competitive analysis.
  • Take a screenshot of the SOV charts and paste them into the slide deck. Make sure the charts are set to “social media”.
Social media tab in share of voice report.Social media tab in share of voice report.

3. Compare pricing and features

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. 

Slide 5 — features vs. pricing.Slide 5 — features vs. pricing.

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.
Filling data in the spreadsheet.Filling data in the spreadsheet.
  • Enter the price of the cheapest plan (excluding free plans). 
Adding pricing data inside the spreadsheet.Adding pricing data inside the spreadsheet.
  • Once finished, copy the chart and paste it inside the deck. 

4. Find strong and weak points based on user reviews

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: 

 Slide 6 — likes and dislikes about Competitors. Slide 6 — likes and dislikes about Competitors.

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.
ChatGPT prompt for competitive analysis.ChatGPT prompt for competitive analysis.
  • 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.
Sample of ChatGPT output with charts.Sample of ChatGPT output with charts.
  • 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. 

5. Compare purchasing convenience

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. 

Slide 7 — purchasing convenience.Slide 7 — purchasing convenience.

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.

Step 6. Present conclusions

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. 

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Critical WordPress Form Plugin Vulnerability Affects Up To +200,000 Installs

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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:

Update ASAP! Critical Unauthenticated Arbitrary File Upload in MW WP Form Allows Malicious Code Execution

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How SEOs Make the Web Better

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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 help regular people benefit from search

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 turn helpful standards into real websites

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.)

Screenshot: Aleyda Solis' helpful content GPTScreenshot: Aleyda Solis' helpful content GPT

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.

SEOs pressure-test Google’s systems

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.

Illustration: how fixing problems leads to smaller future problems and improved search experienceIllustration: how fixing problems leads to smaller future problems and improved search experience

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.

SEOs are the internet’s quality assurance team

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.

SEOs act as a check-and-balance

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.

Screenshot from Patrick Stox's presentation, How Search WorksScreenshot from Patrick Stox's presentation, 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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