Is Google’s Use Of Compressibility An SEO Myth? via @sejournal, @martinibuster

I recently came across an SEO test that attempted to verify whether compression ratio affects rankings. It seems there may be some who believe that higher compression ratios correlate with lower rankings. Understanding compressibility in the context of SEO requires reading both the original source on compression ratios and the research paper itself before drawing conclusions about whether or not it’s an SEO myth.

Search Engines Compress Web Pages

Compressibility, in the context of search engines, refers to how much web pages can be compressed. Shrinking a document into a zip file is an example of compression. Search engines compress indexed web pages because it saves space and results in faster processing. It’s something that all search engines do.

Websites & Host Providers Compress Web Pages

Web page compression is a good thing because it helps search crawlers quickly access web pages which in turn sends the signal to Googlebot that it won’t strain the server and it’s okay to grab even more pages for indexing.

Compression speeds up websites, providing site visitors a high quality user experience. Most web hosts automatically enable compression because it’s good for websites, site visitors and also good for web hosts because it saves on bandwidth loads. Everybody wins with website compression.

High Levels Of Compression Correlate With Spam

Researchers at a search engine discovered that highly compressible web pages correlated with low-quality content. The study called Spam, Damn Spam, and Statistics: Using Statistical Analysis to Locate Spam Web Pages  (PDF) was conducted in 2006 by two of the world’s leading researchers, Marc Najork and Dennis Fetterly.

Najork currently works at DeepMind as Distinguished Research Scientist. Fetterly, a software engineer at Google, is an author of many important research papers related to search, content analysis and other related topics. This research paper isn’t just any research paper, it’s an important one.

What the research paper shows is that 70% of web pages that compress at a level of 4.0 or higher tended to be low quality pages with a high level of redundant word usage. The average compression level of sites was around 2.0.

Here are the averages of normal web pages listed by the research paper:

• Compression ratio of 2.0:
  The most frequently occurring compression ratio in the dataset is 2.0.
• Compression ratio of 2.1:
  Half of the pages have a compression ratio below 2.1, and half have a compression ratio above it.
• Compression ratio of 2.11:
  On average, the compression ratio of the pages analyzed is 2.11.

It would be an easy first-pass way to filter out the obvious content spam so it makes sense that they would do that to weed out heavy-handed content spam. But weeding out spam is more complicated than simple solutions. Search engines use multiple signals because it results in a higher level of accuracy.

The researchers reported that 70% of sites with a compression level of 4.0 or higher were spam. That means that the other 30% were not spam sites. There are always outliers in statistics and that 30% of non-spam sites is why search engines tend to use more than one signal.

Do Search Engines Use Compressibility?

It’s reasonable to assume that search engines use compressibility to identify heavy handed obvious spam. But it’s also reasonable to assume that if search engines employ it they are using it together with other signals in order to increase the accuracy of the metrics. Nobody knows for certain if Google uses compressibility.

Is There Proof That Compression Is An SEO Myth?

Some SEOs have published research analyzing the rankings of thousands of sites for hundreds of keywords. They found that both the top-ranking and bottom-ranked sites had a compression ratio of about 2.4. The difference between their compression ratios was just 2%, meaning the scores were essentially equal. Those results are close to the normal average range of 2.11 reported in the 2006 scientific study.

The SEOs claimed that the mere 2% higher compression levels of the top-ranked sites over the bottom-ranked sites prove that compressibility is an SEO myth. Of course, that claim is incorrect. The average compression ratio of normal sites in 2006 was 2.11, which means the average 2.4 ratio in 2025 falls well within the range of normal, non-spam websites.

The ratio for spam sites is 4.0, so the fact that both sets of top and bottom ranked sites are about 2.4 ratio is meaningless since both scores fall within the range of normal.

If we assume that Google is using compressibility, a site would have produce a compression ratio of 4.0, plus send other low quality signals, to trigger an algorithmic action. If that happened those sites wouldn’t be in the search results at all because they wouldn’t be in the index and therefore there is no way to test that with the SERPs, right?

It would be reasonable to assume that the sites with high 4.0 compression ratios were removed. But we don’t know that, it’s not a certainty.

Is Compressibility An SEO Myth?

Compressibility may not be an SEO myth. But it’s probably not anything publishers or SEOs should be worry about as long as they’re avoiding heavy-handed tactics like keyword stuffing or repetitive cookie cutter pages.

Google uses de-duplication which removes duplicate pages from their index and consolidates the PageRank signals to whichever page they choose to be the canonical page (if they choose one). Publishing duplicate pages will likely not trigger any kind of penalty, including anything related to compression ratios, because, as was already mentioned, search engines don’t use signals in isolation.

U.S. DOJ Antitrust Filing Proposes 4 Ways To Break Google’s Monopoly via @sejournal, @martinibuster

The plaintiffs in an antitrust lawsuit against Google filed a revised proposed final judgment for the judge in the case to consider. The proposal comes after a previous ruling where the court determined that Google broke antitrust laws by illegally maintaining its monopoly.

The legal filing by the plaintiffs, the United States Department Of Justice and State Attorneys General, argue that Google has maintained monopolies in search services and text advertising through anticompetitive practices.

The filing proposes four ways to loosen Google’s monopolistic hold on search and advertising.

1. Requiring Google to separate Chrome from its business—this could mean selling it or spinning it off into an independent company.
2. Limiting Google’s payments to companies like Apple for making Google the default search engine, reducing its ability to secure exclusive deals.
3. Stopping Google from favoring its own products over competitors in search results and other services, ensuring a more level playing field.
4. Increasing transparency in Google’s advertising and data practices so competitors have fairer access to key information.

The proposal asks that Google be subjected to continuous oversight through mandatory reporting to ensure transparency in Google’s advertising and data practices:

“Google must provide to the Technical Committee and Plaintiffs a monthly report outlining any changes to its search text ads auction and its public disclosure of those changes.”

It also suggests ongoing enforcement to guarantee that Google doesn’t impose new restrictions that undermine transparency requirements:

“Google must not limit the ability of advertisers to export in real time (by downloading through an interface or API access) data or information relating to their entire portfolio of ads or advertising campaigns bid on, placed through, or purchased through Google.”

The goal of the above section is to increase transparency in Google’s advertising system and make it easier for advertisers to analyze their ad performance, greater transparency.

Real-time access ensures advertisers can make immediate adjustments to their campaigns instead of waiting for delayed reports and it assures that advertisers aren’t locked into the Google advertising system by holding them hostage to their historical data.

The legal filing requires government-imposed restrictions and changes to Google’s advertising business practices. It proposes remedies for how Google should be regulated or restructured following the court’s earlier ruling that Google engaged in monopolistic practices. However, this is not the final judgment and the court must still decide whether to adopt, modify, or reject these proposed remedies.

Why Google May Adopt Vibe Coding For Search Algorithms via @sejournal, @martinibuster

A new trend in Silicon Valley, Vibe Coding, is driving an exponential acceleration in how quickly engineers can develop products and algorithms. This approach aligns with principles outlined by Google co-founder Sergey Brin in a recent email to DeepMind engineers.

Top Silicon Valley insiders call Vibe Coding the “dominant way to code,” and Brin’s message suggests that Google will embrace it to dramatically speed up AI development. Given its potential, this approach may also extend to Google’s search algorithms, leading to more changes to how search results are ranked.

Vibe Coding Is Here To Stay

The four Y Combinator executives agreed that vibe coding is a very big deal but were surprised at how fast it has overtaken the industry. Jarede Friedman observed that it’s like something out of the fairy tale Jack and the Beanstalk, where the world-changing magic beans sprout into gigantic beanstalks over night.

Garry Tan agreed, saying:

“I think our sense right now is this isn’t a fad. This isn’t going away. This is actually the dominant way to code, and if you’re not doing it, you might be left behind. This is here to stay.”

What Is Vibe Coding?

Vibe coding is software engineering with AI:

• Software engineers use AI to generate code rather than writing it manually.
• Rely on natural language prompts to guide software development.
• Prioritize speed and iteration.
• Time isn’t spent on debugging as code is simply regenerated until it works.
• Vibe coding shifts software engineering focus from writing code to choosing what kinds of problems to solve.
• Leverage AI for rapid code regeneration instead of traditional debugging.
• It is exponentially speeding up coding.

Vibe coding is a way creating code with AI with an emphasis on speed. That means it’s increasingly less necessary to debug code because an engineer can simply re-roll the code generations multiple times until the AI gets it right.

A recent tweet by Andrej Karpathy kicked off a wave of excitement in Silicon Valley. Karpathy, a prominent AI researcher and former director of AI at Tesla, described what Vibe Coding is and explained why it’s the fastest way to code with AI. It’s so reliable that he doesn’t even check the modifications the AI makes (referred to as “diffs”).

Karpathy tweeted:

“There’s a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good.

Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like “decrease the padding on the sidebar by half” because I’m too lazy to find it. I “Accept All” always, I don’t read the diffs anymore.

When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I’d have to really read through it for a while.

Sometimes the LLMs can’t fix a bug so I just work around it or ask for random changes until it goes away. It’s not too bad for throwaway weekend projects, but still quite amusing.

I’m building a project or webapp, but it’s not really coding – I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.”

Sergey Brin Emphasizes Vibe Coding Principles

A recent email from Google co-founder Sergey Brin to DeepMind engineers emphasized the need to integrate AI into their workflow to reduce time spent on coding. The email states that code matters most and that AI will improve itself, advising that if it’s simpler to prompt an AI for a solution, then that’s preferable to training an entirely new model. Brin describes this as highly important for becoming efficient coders. These principles align with Vibe Coding, which prioritizes speed, simplicity, and AI-driven development.

Brin also recommends using first-party code (code developed by Google) instead of relying on open-source or third-party software. This strongly suggests that Google intends to keep its AI advancements proprietary rather than open-source. That may mean any advancements created by Google will not be open-sourced and may not show up in research papers but instead may be discoverable through patent filings.

Brin’s message de-emphasizes the use of LoRA, a machine learning technique used to fine-tune AI models efficiently. This implies that he wants DeepMind engineers to prioritize efficient workflows rather than spending excessive time fine-tuning models. This also suggests that Google is shifting focus toward simpler, more scalable approaches like vibe coding which rely on prompt engineering.

Sergey Brin wrote:

“Code matters most — AGI will happen with takeoff, when the Al improves itself. Probably initially it will be with a lot of human help so the most important is our code performance. Furthermore this needs to work on our own 1p code. We have to be the most efficient coder and Al scientists in the world by using our own Al.

Simplicity — Lets use simple solutions where we can. Eg if prompting works, just do that, don’t posttrain a separate model. No unnecessary technical complexities (such as lora). Ideally we will truly have one recipe and one model which can simply be prompted for different uses.

Speed — we need our products, models, internal tools to be fast. Can’t wait 20 minutes to run a bit of python on borg.”

Those statements align with the principles of vibe coding so it’s important to understand what it is and how it may affect how Google develops search algorithms and AI which may be used for the purposes of ranking websites.

Software Engineers Transitioning To Product Engineers

A recent podcast by Y Combinator, a Silicon Valley startup accelerator company, discussed how vibe coding is changing what it means to be a software engineer and how it will affect hiring practices.

The podcast hosts quoted multiple people:

Leo Paz, Founder of Outlit observed:

“I think the role of Software Engineer will transition to Product Engineer. Human taste is now more important than ever as codegen tools make everyone a 10x engineer.”

Abhi Aiyer of Mastra shared how their coding practices changed:

“I don’t write code much. I just think and review.”

One of the podcast hosts, Jarede Friedman, Managing Partner, Y Combinator said:

“This is a super technical founder who’s last company was also a dev tool. He’s extremely able to code and so it’s fascinating to have people like that saying things like this.

They next quoted Abhi Balijepalli of Copycat:

“I am far less attached to my code now, so my decisions on whether we decide to scrap or refactor code are less biased. Since I can code 3 times as fast, it’s easy for me to scrap and rewrite if I need to.”

Garry Tan, President & CEO, Y Combinator commented:

“I guess the really cool thing about this stuff is it actually parallelizes really well.”

He quoted Yoav Tamir of Casixty:

“I write everything with Cursor. Sometimes I even have two windows of Cursor open in parallel and I prompt them on two different features.”

Tan commented on how much sense that makes and why not have three instances of Cursor open in order to accomplish even more.

The panelists on the podcast then cited Jackson Stokes of Trainloop who explains the exponential scale of how fast coding has become:

“How coding has changed six to one months ago: 10X speedup. One month ago to now: 100X speedup. Exponential acceleration. I’m no longer an engineer, I’m a product person.”

Garry Tan commented:

“I think that might be something that’s happening broadly. You know, it really ends up being two different roles you need. It actually maps to how engineers sort of self assign today, in that either you’re front-end or backend. And then backend ends up being about actually infrastructure and then front-end is so much more actually being a PM (product manager)…”

Harj Taggar, Managing Partner, Y Combinator observed that the LLMs are going to push people to the role of making choices, that the actual writing of the code will become less important.

Why Debugging With AI Is Unnecessary

An interesting wrinkle in Code Vibing is that one of the ways it speeds up development is that software engineers no longer have to spend long hours debugging. In fact, they don’t have to debug anymore. This means that they are able to push code out the door faster than ever before.

Tan commented on how poor AI is at debugging:

“…one thing the survey did indicate is that this stuff is terrible at debugging. And so… the humans have to do the debugging still. They have to figure out well, what is the code actually doing?

There doesn’t seem to be a way to just tell it, debug. You were saying that you have to be very explicit, like as if giving instructions to a first time software engineer.”

Jarede offered his observation on AI’s ability to debug:

“I have to really spoon feed it the instructions to get it to debug stuff. Or you can kind of embrace the vibes. I’d say Andrej Karpathy style, sort of re-roll, just like tell it to try again from scratch.

It’s wild how your coding style changes when actually writing the code becomes a 1000x cheaper. Like, as a human you would never just like blow away something that you’d worked on for a very long time and rewrite from scratch because you had a bug. You’d always fix the bug. But for the LLM, if you can just rewrite a thousand lines of code in just six seconds, like why not?”

Tan observed that it’s like how people use AI image generators where if there’s something they don’t like they just reiterate without even changing the prompt, they just simply click re-roll five times and then at the fifth time it works.

Vibe Coding And Google’s Search Algorithms

While Sergey Brin’s email does not explicitly mention search algorithms, it advocates AI-driven, prompt-based development at scale and high speed. Since Vibe Coding is now the dominant way to code, it is likely that Google will adopt this methodology across its projects, including the development of future search algorithms.

Watch the Y Combinator Video Roundtable

Vibe Coding Is The Future

Featured Image by Shutterstock/bluestork

Google CTR Study: AI Overviews Rise As Click Rates Decline via @sejournal, @MattGSouthern

A new study on Google search behavior examines changes in clickthrough rates across industries. The data correlates with increased AI Overviews (AIOs) in Google’s search results.

Research from Advanced Web Ranking (AWR) reveals that AIOs appeared in 42.51% of search results in Q4, up 8.83 percentage points from the previous quarter.

With this increase, clickthrough rates for informational queries dropped significantly.

Websites in the top four positions for searches using terms like what, when, where, and how saw a combined decrease of 7.31 percentage points in desktop clickthrough rates.

Study author Dan Popa states:

“This surge in AI Overviews may be impacting clickthrough rates for organic listings, as informational content is increasingly getting overrun by these AI-generated summaries.”

Here’s more about the study and what the findings mean for your website.

Industry CTR Gap

The study reveals SEO success is becoming increasingly industry-dependent.

For example, law and politics sites recorded a 38.45% CTR in position one, while science sites get 19.06% for the same ranking. That gap nearly tripled in a single quarter.

CTR shifts were observed in the following sectors:

• Law & Politics: Recorded Q4’s highest position-specific increase with a 7.39 percentage point CTR gain for top desktop positions, alongside 68.66% higher search demand.
• Science: Recorded Q4’s largest CTR decline with top desktop positions dropping 6.03 percentage points, while experiencing a 37.63% decrease in search demand.
• Careers: Despite search demand more than tripling (+334.36%), top three desktop positions lost a combined 4.34 percentage points in CTR.
• Shopping: The holiday season brought a 142.88% surge in search demand, yet top-ranked sites saw CTR declines of 1.39 and 1.96 percentage points on desktop and mobile, respectively.
• Education: Mixed bag with top positions gaining nearly 6% in CTR while positions 2-3 declined, all during a traffic increase.

Only the business and style and fashion sectors saw increased search demand and improved CTRs, making them rare bright spots in a challenging market.

Desktop vs. Mobile

The report also looks at behavior patterns between devices.

While desktop CTR for informational queries declined, mobile showed opposing trends, with top-ranked sites gaining 1.81 percentage points.

Similar device-specific shifts appeared across multiple industries. For example, arts and entertainment websites saw a 1.01 percentage point drop in desktop CTR but a 2.28 percentage point mobile gain for position one.

Query length also influenced click behavior differently across devices.

Long-tail queries (four or more keywords) experienced CTR declines on desktop for positions 2-3. In contrast, single-word queries gained nearly two percentage points in CTR on mobile for top positions.

Why This Study Matters

These findings demonstrate that ranking #1 doesn’t guarantee the same traffic it once did. Your industry, query type, and SERP features (especially AI Overviews) all impact click potential.

AWR suggests tracking pixel depth (how far users must scroll to see your listing) alongside rankings for more accurate traffic forecasting.

It’s important to account for these widening performance gaps, particularly for informational content competing with Google’s AIOs.

Study Methodology

Advanced Web Ranking’s research compared CTR averages from Q4 2024 to Q3 2024. It included data from markets like the US and UK, linking CTR shifts with industry search demand.

Using AWR’s free AIO tool, the study found an 8.83 percentage point rise in AI Overview presence. Queries were categorized by intent, length, and 21 industry verticals to identify user behavior patterns.

For more, read the full study.

Featured Image: jack_the_sparrow/Shutterstock