Mar 11 2025
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.

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Mar 11 2025
Deep SEO: The Potential Impact Of AI Mode And Deep Search Models via @sejournal, @Kevin_Indig

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Last week, Google officially launched AI Mode, an AI Overview on steroids, in beta.

Almost one year after the launch of AI Answers in the search results, we have enough data to show that the net impact on the open web is as positive as Trump’s tariffs on the U.S. economy. Spoiler: Not very positive.

AI Overviews take more clicks away from classic search results than they give back to cited sources. AI Mode has the potential to rip the gaping trade deficit AI Overviews are already causing on traffic wide open.

I argue that the appearance of AI Mode is the natural response to Deep Search and the potential outlook of the future of AI Search.

Image Credit: Lyna ™

Deep Research Threatens Google

Deep Search is an AI Agent by Open AI that creates lengthy reports about a subject of your choice: “An agent that uses reasoning to synthesize large amounts of online information and complete multi-step research tasks for you.”

The most obvious application is market research, but the agent can also deliver rich insights into consumer topics like buying a car, booking a trip, or getting a credit.

Deep research is built for people who do intensive knowledge work in areas like finance, science, policy, and engineering and need thorough, precise, and reliable research. It can be equally useful for discerning shoppers looking for hyper-personalized recommendations on purchases that typically require careful research, like cars, appliances, and furniture.

Image Credit: Kevin Indig

Deep Search performs tens to hundreds of searches to compile a report. I’ve tried prompts for purchase decisions.

When I asked for “the best hybrid family car with 7 seats in the price range of $50,000 to $80,000,” Deep Research browsed through 41 search results and reasoned its way through the content.

Some examples of search reasoning:

I found a Kelley Blue Book article on 7-seat hybrids. It’s a good starting point, though not all details are always included.

I’m digging into cargo capacities for the Toyota Highlander and Grand Highlander. The Grand Highlander is proving to be more spacious, making it a solid option for larger families.

I’m looking at the XC90 Recharge’s electric and combined ranges, and the discrepancies in MPGe figures from different sources.

The report took 10 minutes to put together but probably saved human hours of research and at least 41 clicks – clicks that could’ve gone to Google ads.

The Case For Deep Search

Are Deep Research agents a threat to Google? I think so.

Here’s why:

  1. The results are impressive, and the time savings are massive. Early on, Google bragged about how quickly it put the search results together. But that was speed to results, not speed to answer. Today, Deep Search agents take minutes to get an answer, but it’s all you need.
  2. There is massive potential for personalization, from sources to search criteria.
  3. Conversational back and forth, just like with a salesperson in a store. Deep Re-search agents provide a concise summary that users can expand and explore at their own pace.
  4. It turns out that every search engine or AI chatbot either already has a Deep Search agent or is working on one. It could truly be the future of Search for complex queries.
Image Credit: Kevin Indig

Bing had a “Deep Search” feature since December 2023! And it does exactly what the name promises, just faster and not as deep as ChatGPT’s agent.

Today’s search engines are powerful tools that help us find information on the web, but sometimes they fall short of our expectations. When we have complex, nuanced, or specific questions, we often struggle to find the answers we need. We ourselves know what we’re looking for, but the search engine just doesn’t seem to understand.

That’s why we created deep search, a new Microsoft Bing feature that provides even more relevant and comprehensive answers to the most complex search queries. Deep search is not a replacement for Bing’s existing web search, but an enhancement that offers the option for a deeper and richer exploration of the web.1

I didn’t think I’d live long enough to see the day that Google copies Bing … But they’re not alone.

Grok has “Deep Search” and Gemini and Perplexity have “Deep Research.” Everyone is copying each other, and they’re not even putting in the effort to choose a different name. What a strong sign of commoditization.

Google’s AI Mode (source)

My theory: Google modeled AI Mode after Bing’s Deep Search after seeing what ChatGPT’s Deep Search can do.

Using a custom version of Gemini 2.0, AI Mode is particularly helpful for questions that need further exploration, comparisons and reasoning. You can ask nuanced questions that might have previously taken multiple searches — like exploring a new concept or comparing detailed options — and get a helpful AI-powered response with links to learn more.2

Interestingly, AI Mode has the opposite of AI Overviews: In Google’s Q3 earnings announcement, Sundar PichAI said Google sees an “increase in search usage among people who use the new AI overviews”.3

So, AI Overviews lead to more searches, but AI Mode saves users time and queries:

You can ask nuanced questions that might have previously taken multiple searches — like exploring a new concept or comparing detailed options — and get a helpful AI-powered response with links to learn more.4

I don’t think we’ll ever go back to the pre-AI way of search. The universal key challenge of AI answers, whatever their form, is trust. The obvious problem is hallucination.

It’s ironic that ChatGPT Deep Research tells me it browsed through 29 sources, but when I counted, I found 41.

However, reasoning models are getting better at solving this problem with raw computing, i.e., by “thinking harder” about their answers.

The bigger solvable problem for Deep Search agents is source selection.

Untrustworthy sources are the microplastics of AI answers. There is a good reason why all reasoning models openly show their reasoning.

Even though we might pay as much attention to the reasoning details as to any Terms of Service, they make us feel like a lot is happening in the background.

Perception is important for trust. However, source selection is a very solvable problem: Users can simply tell the model to ignore the sources they don’t want, and the model memorizes that behavior over time.

Two less solvable problems remain:

  • Bias: In my analysis of AI chatbot research, I pointed out that LLMs have a bias towards global brands, luxury brands, corporate sources and prompt sentiment.
  • Access: Information needs to be on the internet for Deep Search agents to find it (that’s where Google and Bing have a big competitive advantage).

The biggest question, of course, is whether Deep Search Agents will find broad adoption or stay in the knowledge worker bubble.

AI Mode could bring it to the masses and drive the stake deeper into the heart of informational clicks.

The Impact On SEO

AI Overviews spiked in November ‘24 and February ‘25 (Image Credit: Kevin Indig)

The impact of AI Overviews on SEO traffic is negative.

In my meta-analysis of 19 studies about AI Overviews, I found that AIOs reduce click-through rates across the board. Will AI Mode make it worse? Most likely. But there is hope.

First of all, Deep Search agents are very transparent with their sources and sometimes queries.

ChatGPT’s Deep Search literally calls out what it’s searching for, so we can hopefully track and optimize for these queries. So far, LLMs still rely on search results a lot.

Second, only because searchers get answers before clicking to websites, their purchase intent doesn’t go away.

What goes away for marketers is the ability to influence buyers on their website before they buy  – as long as AI chatbots don’t offer a direct checkout.

We’ll need to find other ways to influence buyers: brand marketing, Reddit, YouTube, social media, advertising.

Third, there is a chance that AI Mode shows up predominantly for informational keywords, just like AI Overviews. In that case, a lot of weight will fall on high-intent keywords, like “buy x” or “order y.”

Fourth, Bing doesn’t separate the Deep Search answer but parks it in the middle of organic and paid results, garnished with links to sources. Hopefully, users will still click outside the deep answer.

I wonder how Google plans to monetize AI Mode, which must be more costly and resource-intensive.

To be fair, Google reduced the cost of an AI Overview by 90%, which tells me they figured out the unit economics. So, it’s possible.

But could this be an opportunity to bring the idea of monetizing Search partially with subscriptions back on the table?

Based on a report by The Information, OpenAI considers charging “up to $20,000 per month for specialized AI agents” that could perform PhD level research, $10,000 for a software developer agent, and $2,000 for a knowledge worker agent.5

Still a long way to go, but it brings up a nice theory about AI Mode: What if Google users could pay for better models that give better answers, or have better skills?

1 Introducing deep search

2 Expanding AI Overviews and introducing AI Mode

3 Q3 earnings call: CEO’s remarks

4 Expanding AI Overviews and introducing AI Mode

5 OpenAI Plots Charging $20,000 a Month For PhD-Level Agents

Featured Image: Paulo Bobita/Search Engine Journal

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Mar 11 2025
Ask An SEO: How Can You Distinguish Yourself In This Era Of AI Search Engines? via @sejournal, @HelenPollitt1

Today’s question comes from FC, who asks:

“As an SEO specialist for over 6 years now, what and where does one need to focus with regard to SEO in this current dispensation.

How can you distinguish yourself and standout as an SEO specialist in this era of generative AI and AI search engines?”

This is an excellent question because it goes right to the heart of concerns I hear from a lot of SEO professionals. They have managed to build a solid career and name for themselves as an SEO specialist, but it now feels like the game has changed.

They worry that the skills and experience that got them to this point will not be enough to keep them excelling.

I want to address those concerns, both from the perspective of job seekers and those looking to make an impression in their current role.

What’s Changed

Up until a couple of years ago, it felt like there were clear career choices for SEO specialists to make.

Employed or self-employed? In-house or agency? Technical SEO or content SEO? Small business or enterprise sites? People manager or hands-on practitioner?

These series of decisions, or simply circumstances we found ourselves in, shaped our career paths.

There were central components to SEO. Primarily, you would be working with Google. You would be measured on key performance indicators (KPIs) like clicks and conversions.

You could impress stakeholders by linking your work directly to revenue.

It doesn’t seem as simple as that now, though.

LLMs And Social Media

More recently, there has been a focus on looking at optimizing brands’ presence in other search platforms, not just Bing, Yandex, Baidu, and other regionally relevant search engines.

It now includes platforms not traditionally thought of as belonging to the purview of SEO: TikTok, Perplexity AI, and app stores.

KPIs And Metrics

Google’s walled garden is growing larger, and proving the worth of SEO is getting harder. It’s increasingly difficult to show growth in your share of organic clicks when the pot is getting smaller.

With more answers being given in the search results themselves, and a reduction in the need for clicks off the SERPs, tracking the impact of SEO isn’t straightforward.

With potential – and current – employers still looking at year-on-year clicks, impressions, and revenue growth as their measure of an SEO’s success, this makes standing out quite challenging.

The Skills That Remain Important

I fundamentally believe that the foundational principles of SEO remain unchanged.

However, how we apply them may change with the advent of LLMs and other search platforms.

Technical SEO

A crawl issue that is preventing Googlebot smartphone from accessing the key pages on your site will likely also affect PerplexityBot and OpenAI’s OAI-SearchBot.

As an SEO, we will need to be able to identify where these bots are struggling to crawl pages. We will need to find solutions that enable them to access the pages we want to have served in their search results.

To stand out, make sure you are not just thinking Google-first with your technical solutions.

Consider the other sources of traffic, like LLMs and social media, which might be impacted by the decisions you are making.

Ensure you are also tracking and reporting on the impact of these changes across these other platforms.

Content SEO

Understanding what content searchers are looking for, how search engines perceive it, and what they are choosing to serve as search results is a fundamental aspect of SEO. This won’t change.

However, how you discuss it and the actions you take will change.

From now on, not only are the Google algorithms important for how you create and optimize content, but so are a host of other algorithms.

You will need to consider how searchers are surfacing content through other search platforms. You will also need to know how to make sure your content is served as the result.

Make sure you are moving away from Google as the only algorithm to optimize for and towards the other drivers of traffic and visibility.

Digital PR

I would suggest that digital PR is becoming even more important.

As the search engines we are optimizing for become more numerous, the key factor that seems to unite them is a reward of “authority.”

That is, to give your content a chance of being served as a result in any search engine, it needs to be perceived as authoritative on the subject.

These newer search platforms will still need to use similar methods to Google in identifying expertise and authoritativeness. Digital PR will be key in that.

I do feel that we need to stop making backlinks the main priority of digital PR, however.

Instead, we need to start focusing on how we report on mentions, citations, and conversations about brands and products.

For example, we can look at social media engagement metrics as an indicator of authority. Brand perception may well be formed through forum discussions, reviews, and comments on social media sites.

Just because we know that Googlebot discounts links from some social media platforms in attributing authority doesn’t mean that the newer search engines will. Indeed, they will not rely on social media sites heavily to understand brands.

For now, set yourself apart by rethinking the purpose of digital PR for SEO. Look at the benefits to the brand as a whole and start factoring this into your strategies.

“Soft” Skills

I maintain that the most successful SEO professionals are those who have mastered the non-SEO-specific skills that make businesses work.

Strategic thinking, stakeholder management, and leadership skills are all critical to success not only in SEO, but also in any career.

To really stand out in the changing SEO industry, focus on how these skills will need to be applied.

For example, factor in social media and LLMs into your SEO strategies. Make sure you are not just focusing on Google, but introducing the idea that SEO is broader than that.

Make sure you are liaising with development teams to loop them into your ideas for how to make the site accessible to AI bots. Work on being a thought leader in LLMs and new search platforms for your company.

These sorts of skills are those that will really make you stand out, but you need to apply them with the future of SEO in mind. Future-proof your careers as well as your websites!

Cross-Platform Knowledge

This is probably the hardest one for some SEO specialists to do. Stop looking at Google as the source of all SEO performance and widen the net.

Get comfortable with the other AI search platforms that are beginning to send traffic to your site. Use them yourself, and get familiar with what sort of content they serve and why.

Use social media sites and forums that are where your audience discusses brands like yours. Make sure that you are aware of how they work, and how to participate in those discussions without negative backlash.

Stand out by looking outside of the narrow “Google is SEO” box.

Being An Expert In The New Era Of SEO

How, then, can you guarantee that you are still perceived as an expert in SEO while the goalposts are changing?

What will make you stand out when you are applying for new jobs right now?

How can you prove that your skillset is still relevant whilst others are proclaiming “SEO is dead” (again)?

Demonstrate Impact Through Other Channels

Look at how you can collaborate more with adjacent channels.

For example, I’ve mentioned that social media and forums will be key areas where LLMs will discern brand relevancy and trustworthiness. Work with your teams who are already on those platforms.

Start helping them in areas that you are already an expert, for example: understanding algorithms, creating optimized content and measuring brand authority.

Drive impact in those areas and report on it alongside your more traditional SEO metrics.

Demonstrate Impact Through Other Metrics That Still Line Up With Corporate Goals

Although we are used to reporting on metrics like clicks, rankings, and impressions for SEO, we may need to start looking at other metrics if we want to continue showing the worth of SEO.

For example, consider utilizing tools like Otterly and Goodie to measure visibility in AI search platforms. Or, at the very least, some of the more traditional search engine rankings tools also cover Google’s AI Overview visibility.

Use these tools to demonstrate how the work you are doing is impacting the brand’s performance in AI search platforms.

Continue to relate all work you do back to revenue, or other core conversion goals for your business. Don’t forget to show how traffic from LLMs is converting on your site.

Continue Learning

A key way to stand out in your SEO career at the moment is to show a willingness to upskill and diversify your skillset.

The SEO landscape is shifting, and as such, it’s important to stay on top of new platforms and how they work.

Make sure you are utilizing training that is available on LLM optimization. Use the platforms yourself so you can understand what search real estate is available on them.

Share your findings in interviews and discussions with colleagues so you are highlighting what you’ve learned.

Although this may seem basic, you may find there are a lot of SEO professionals out there with their heads still buried in the sand when it comes to the evolution of the discipline.

Stand Out By Being Adaptable

At the end of the day, SEO is changing. That doesn’t mean that the skills we’ve developed over the past years are obsolete.

Instead, they are even more in demand as new platforms promise new avenues to reach prospective audiences.

The best way to stand out as an SEO in the current era of SEO is by being adaptable.

Learn how to apply your SEO skills to these emerging platforms and track your success.

More Resources:

Featured Image: Paulo Bobita/Search Engine Journal

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Mar 11 2025
The cheapest way to supercharge America’s power grid

US electricity consumption is rising faster than it has in decades, thanks in part to the boom in data center development, the resurgence in manufacturing, and the increasing popularity of electric vehicles. 

Accommodating that growth will require building wind turbines, solar farms, and other power plants faster than we ever have before—and expanding the network of wires needed to connect those facilities to the grid.

Heat Exchange

MIT Technology Review’s guest opinion series, offering expert commentary on legal, political and regulatory issues related to climate change and clean energy. You can read the rest of the pieces here.

But one major problem is that it’s expensive and slow to secure permits for new transmission lines and build them across the country. This challenge has created one of the biggest obstacles to getting more electricity generation online, reducing investment in new power plants and stranding others in years-long “interconnection queues” while they wait to join the grid.

Fortunately, there are some shortcuts that could expand the capacity of the existing system without requiring completely new infrastructure: a suite of hardware and software tools known as advanced transmission technologies (ATTs), which can increase both the capacity and the efficiency of the power sector.

ATTs have the potential to radically reduce timelines for grid upgrades, avoid tricky permitting issues, and yield billions in annual savings for US consumers. They could help us quickly bring online a significant portion of the nearly 2,600 gigawatts of backlogged generation and storage projects awaiting pathways to connect to the electric grid. 

The opportunity to leverage advanced transmission technologies to update the way we deliver and consume electricity in America is as close to a $20 bill sitting on the sidewalk as policymakers may ever encounter. Promoting the development and use of these technologies should be a top priority for politicians in Washington, DC, as well as electricity market regulators around the country.

That includes the new Trump administration, which has clearly stated that building greater electricity supply and keeping costs low for consumers are high priorities. 

In the last month, Washington has been consumed by the Trump team’s efforts to test the bounds of executive power, fire civil servants, and disrupt the basic workings of the federal government. But when or if the White House and Congress get around to enacting new energy policies, they would be wise to pick up the $20 bill by enacting bipartisan measures to accelerate the rollout of these innovative grid technologies.

ATTs generally fall into four categories: dynamic line ratings, which combine local weather forecasts and measurements on or near the transmission line to safely increase their capacity when conditions allow; high-performance conductors, which are advanced wires that use carbon fiber, composite cores, or superconducting materials to carry more electricity than traditional steel-core conductors; topology optimization, which uses software to model fluctuating conditions across the grid and identify the most efficient routes to distribute electricity from moment to moment; and advanced power flow control devices, which redistribute electricity to lines with available capacity. 

“This would allow utilities to earn a profit for saving money, not just spending it, and could save consumers billions on their electricity bills every year.”

Other countries from Belgium to India to the United Kingdom are already making large-scale use of these technologies. Early projects in the United States have been remarkably successful as well. One recent deployment of dynamic line ratings increased capacity by more than 50% for only $45,000 per mile—roughly 1% of the price of building new transmission.

So why are we not seeing an explosion in ATT investment and deployment in the US? Because despite their potential to unlock 21st-century technology, the 20th-century structure of the nation’s electricity markets discourages adoption of these solutions. 

For one thing, under the current regulatory system, utilities generally make money by passing the cost of big new developments along to customers (earning a fixed annual return on their investment). That comes in the form of higher electricity rates, which local public utility commissions often approve after power companies propose such projects.

That means utilities have financial incentives to make large and expensive investments, but not to save consumers money. When ATTs are installed in place of building new transmission capacity, the smaller capital costs mean that utilities make lower profits. For example, utilities might earn $600,000 per year after building a new mile of transmission, compared with about $4,500 per mile annually after installing the equipment and software necessary for line ratings. While these state regulatory agencies are tasked with ensuring that utilities act in the best interest of consumers, they often lack the necessary information to identify the best approach for doing so.

Overcoming these structural barriers will require action from both state and federal governments, and it should appeal to Democrats and Republicans alike. We’ve already seen some states, including Minnesota and Montana, move in this direction, but policy interventions to date remain insufficient. In a recent paper, we propose a new approach for unlocking the potential of these technologies.

First, we suggest requiring transmission providers to use ATTs in some “no regrets” contexts, where possible downsides are minor or nonexistent. The Federal Energy Regulatory Commission, for example, is already considering requiring dynamic line ratings on certain highly congested lines. Given the low cost of dynamic line ratings, and their clear benefit in cases of congestion, we believe that FERC should quickly move forward with, and strengthen, such a rule. Likewise, the Department of Energy or Congress should adopt an efficiency standard for the wires that carry electricity around the country. Every year, approximately 5% of electricity generated is lost in the transmission and distribution process. The use of high-performance conductors can reduce those losses by 30%.

In addition, federal agencies and state lawmakers should require transmission providers to evaluate the potential for using ATTs on their grid, or provide support to help them do so. FERC has recently taken steps in this direction, and it should continue to strengthen those actions. 

Regulators should also provide financial incentives to transmission providers to encourage the installation of ATTs. The most promising approach is a “shared savings” incentive, such as that proposed in the recent Advancing GETS Act. This would allow utilities to earn a profit for saving money, not just spending it, and could save consumers billions on their electricity bills every year.

Finally, we should invest in building digital tools so transmission owners can identify opportunities for these technologies and so regulators can hold them accountable. Developing these systems will require transmission providers to share information about electricity supply and demand as well as grid infrastructure. Ideally, with such data in hand, researchers can develop a “digital twin” of the current transmission system to test different configurations of ATTs and help improve the performance and efficiency of our grids. 

We are all too aware that the world often faces difficult policy trade-offs. But laws or regulations that facilitate the use of ATTs can quickly expand the grid and save consumers money. They should be an easy yes on both sides of the aisle.

Brian Deese is an innovation fellow at the Massachusetts Institute of Technology and served as director of the White House National Economic Council from 2021 to 2023. Rob Gramlich is founder and president of Grid Strategies and was economic advisor to the chairman of the Federal Energy Regulatory Commission during the George W. Bush administration.

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Mar 11 2025
The Download: supercharging the power grid, and a new Chinese AI agent

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The cheapest way to supercharge America’s power grid

—Brian Deese is an innovation fellow at the Massachusetts Institute of Technology and served as director of the White House National Economic Council from 2021 to 2023. Rob Gramlich is founder and president of Grid Strategies and was economic advisor to the chairman of the Federal Energy Regulatory Commission during the George W. Bush administration.

US electricity consumption is rising faster than it has in decades. Accommodating that growth will require building wind turbines, solar farms, and other power plants faster than we ever have before—and expanding the network of wires needed to connect those facilities to the grid.

But one major problem is that it’s expensive and slow to secure permits for new transmission lines and build them across the country. Fortunately, there are some shortcuts that could expand the capacity of the existing system without requiring completely new infrastructure: a suite of hardware and software tools known as advanced transmission technologies (ATTs), which can increase both the capacity and the efficiency of the power sector.

ATTs have the potential to radically reduce timelines for grid upgrades, avoid tricky permitting issues, and yield billions in annual savings for US consumers. So why are we not seeing an explosion in ATT investment and deployment in the US? Read the full story.

Interested in learning more about this topic? Read more of our stories:

+ What’s driving electricity demand? It isn’t just AI and data centers.

+ That said, AI’s search for energy is growing more urgent. 

+ Why this developer won’t quit fighting to connect the US’s grids. 

+ Here are four ways AI is making the power grid faster and more resilient. 

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 China claims to have created the world’s first fully autonomous AI agent 
The agent, called Manus, can allegedly operate fully free of human intervention. (Forbes)
+ But it’s not clear if the hype can be justified at this stage. (TechCrunch)
+ Two former DeepMind researchers are chasing superintelligence. (Bloomberg $)
+ Four Chinese AI startups to watch beyond DeepSeek. (MIT Technology Review)

2 Meta went to extreme lengths to win China’s approval
Including developing a censorship system to comply with the CCP. (WP $)
+ However, its attempts to curry favor with the party did not bear fruit. (Gizmodo)

3 Anonymous Chinese investors are quietly funding Elon Musk’s ventures
They’re happy to invest tens of millions—so long as their identities remain under wraps. (FT $)
+ Despite the influx of cash, SpaceX isn’t having a great year. (NYT $)
+ Starlink is reaping the benefits of its founder’s proximity to the White House. (NBC News)

4 Ukraine doesn’t have minable rare earths
And even if it did, it would take at least 15 years to reach them. (IEEE Spectrum)
+ The country is preparing to hold negotiations with the US this week. (Economist $)

5 Farewell, the Athena lunar lander
It landed sideways in a crater and has been officially written off. (The Register)
+ Intuitive Machines, the company behind it, is contracted for another two landings. (AP News)
+ Firefly Aerospace, another private firm, had better luck. (Economist $)

6 The American public really doesn’t like DOGE
And Donald Trump is starting to pay attention. (The Atlantic $)
+ Musk represents the problem he is claiming he wants to solve. (Wired $)
+ The Trump administration is threatening scientific progress. (New Yorker $)
+ Anti-Musk protestors are targeting Tesla stores and infrastructure. (WP $)

7 Wikipedia is struggling to document the war in the Middle East
Certain editors have been forbidden from working on related pages. (Bloomberg $)

8 How to store the world’s data
Hard discs seem the obvious choice—for now. (WSJ $)
+ Music labels are going after the Internet Archive for copyright infringement. (Ars Technica)
+ The race to save our online lives from a digital dark age. (MIT Technology Review)

9 YouTube bros are peddling Taliban tourism
Inside the depressing rise of videos purporting to show “another side to Afghanistan.” (Insider $)

10 Amazon and Google’s AI calls Mein Kampf “a true work of art” 
That’s what happens when you search for positive reviews of the Nazi manifesto. (404 Media)
+ Want AI that flags hateful content? Build it. (MIT Technology Review)

Quote of the day

“The US won the internet and the US should win crypto.”

—Tyler Winklevoss, who runs crypto exchange Gemini with his twin brother Cameron, could not be happier with the outcome of Donald Trump’s crypto summit, according to a post on X.

The big story

How this Turing Award–winning researcher became a legendary academic advisor

October 2023

Every academic field has its superstars. But a rare few achieve superstardom not just by demonstrating individual excellence but also by consistently producing future superstars.

Computer science has its own such figure: Manuel Blum, who won the 1995 Turing Award—the Nobel Prize of computer science. He is the inventor of the captcha—a test designed to distinguish humans from bots online.

Three of Blum’s students have also won Turing Awards, and many have received other high honors in theoretical computer science. More than 20 hold professorships at top computer science departments. So what’s the formula to his success? Read the full story.

—Sheon Han

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ Looking for some books to make you laugh out loud? Look no further.
+ What can’t White Lotus star Walton Goggins live without? An orange pen and 22-year old sand, apparently.
+ When it’s time to take a break, here’s how to recharge properly.
+ $40 for “magic” yogurt? What the hell, sure.

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Mar 11 2025
Create Google Ads Scripts via GenAI

I’ve addressed the benefits of Google Ads scripts to automate aspects of account management. Producing custom scripts is now much easier with generative AI platforms.

Scripts can go beyond automation to solve problems. For example, it’s not always evident when an account stops serving ads. The payment method may fail, or Google could reject them. The “Account Down” script runs hourly and sends an email when an account hasn’t seen an impression in a designated period.

I’ve struggled with campaigns that set a higher target cost per acquisition than the budget. It’s not always obvious. A budget might be $500, but the tCPA is $600. The tCPA should be at least three times the budget in my experience.

AI for Ad Scripts

Here’s where AI comes in. Tools such as ChatGPT, Gemini, and Claude (my favorite) can generate scripts quickly.

Sample prompt in Claude for the tCPA mismatch and Claude's response.

Generative AI platforms such as Claude can produce scripts quickly.

Here’s my prompt in Claude to create a script:

Please write a Google Ads script that reviews all campaigns and sends an email alert when the tCPA is higher than the campaign budget.

Here’s Claude’s response:

Google Ads tCPA Alert Script

This script:

1. Iterates through all enabled campaigns in your Google Ads account
2. Identifies campaigns using Target CPA bidding strategy
3. Compares each campaign’s tCPA value against its daily budget
4. Creates a list of campaigns where tCPA exceeds the budget
5. Sends you an email alert with details if any campaigns meet this condition

I pasted the script into Google Ads and received an error.

3/5/2025 3:47:20 PM TypeError: campaign.bidding(...).get is not a function
at main (Code:39:40)
at Object. (adsapp_compiled:20555:54)

I then prompted Claude to fix the error. Here’s Claude’s response:

Google Ads tCPA Alert Script – Fixed

I’ve fixed the error by correcting how the script retrieves the target CPA value. The issue was with the line `var bidding = campaign.bidding().get();` which was using an incorrect method call.

Key changes made:

1. Replaced the problematic method chain with a direct call to `campaign.bidding().getTargetCpa()`

2. Added error handling with try/catch blocks to gracefully handle different API versions and bidding strategy configurations

3. Added support for retrieving tCPA from shared bidding strategies (which have a different access method)

4. Added validation to ensure tCPA was successfully retrieved before comparing it to the budget

The script — download it here — now runs in Google. I solved a problem through a series of prompts.

Not perfect

My next AI task was to obtain a script that identified all active ad groups in live campaigns with no impressions in 21 days. The alert would trigger me to investigate the cause.

Claude’s script worked, but it had two errors:

  • It defaulted to any period, not 21 days.
  • It included custom experiments that ended 21 or more days previously. Experiments run only for a designated period, but the script included them as normal campaigns.

After sharing these errors and receiving replacement scripts, Claude produced new errors for invalid inputs. I ended the session without securing the script I wanted.

It’s a handy reminder that AI is not always as productive as humans. A developer could likely set up this script without the extended back-and-forth.

Claude’s script for tCPA conflicts worked. The no-impressions script did not.

Monitor the unexpected

The final script I created addressed rejected ads. Advertisers should monitor not just the ads but rejected keywords and audiences, which occur occasionally.

Any script in Google Ads times out after 30 to 60 minutes. I first prompted Claude to identify disapproved ad assets (sitelinks, callouts, and more). Fearing a timeout, I opted for two separate scripts.

Dialog showing the prompt and Claude's response.

The follow-up script and response focused on “limited” statuses.

Here’s the prompt for the non-asset disapproval.

Please update this script so it only looks at disapproved ads, keywords, and audiences. Entities with “limited” status should not be included.

And Claude’s response:

I’ll update the script to only include items with “DISAPPROVED” status and exclude those with “LIMITED” or any other non-disapproved status. Here’s the modified version:

Google Ads Script for Disapproved Items Only. Click to open code.

The key changes I made to the script:

1. Changed the condition checks for ads from:

```javascript
if (ad.getPolicyApprovalStatus() != 'APPROVED') {
```

to:

```javascript
if (ad.getPolicyApprovalStatus() == 'DISAPPROVED') {

Download the script.

No limits

My example scripts above alert advertisers via email, but I could have prompted Claude to output to a spreadsheet instead. I could have included paused entities. The possibilities are seemingly limitless.

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Mar 10 2025
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.

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Mar 10 2025
YouTube’s Creator Liaison Shares Advice For Mid-Roll Ad Changes via @sejournal, @MattGSouthern

YouTube Creator Liaison Rene Ritchie has advised content creators on adapting to YouTube’s upcoming mid-roll advertising changes.

These changes take effect on May 12 and will alter how ads appear within videos.

Background

Starting May 12, YouTube will implement a new system prioritizing mid-roll ad placements during natural content breaks rather than at potentially disruptive moments.

YouTube will automatically place ads at natural transitions in videos, but creators can manually control ad placements if they prefer.

This update introduces a hybrid approach, allowing creators to use automatic and manual mid-roll placements simultaneously.

According to YouTube’s early testing, channels adopting this combined approach have seen an average increase in ad revenue of 5%.

Ritchie’s Adaptation Strategy

Sharing his approach on X, Ritchie outlined specific steps he’s taking with his own YouTube channel:

“I’m turning on auto mid-rolls, since that system will continue to be improved and optimized by launch and over time. For new videos, I’m manually inserting additional slots if and as needed where I think it’ll provide the best experience for viewers.”

For existing content, Ritchie recommends a prioritized approach, stating:

“For back catalog, I’m sorting by current watch time and doing the same for the top 20-50 most-watched videos.”

Maintaining Creator Control

Ritchie addressed concerns about YouTube potentially removing manual placement options:

“No one is taking away manual mid-roll placements. Creators can still put slots wherever and whenever we want.”

He reminded creators that designated ad slots don’t guarantee ad placement but indicate where ads can potentially appear.

Ritchie drew a parallel to YouTube’s retention analytics and explained how the new ad feedback tool provides valuable insights.

“In the days before the retention graph in Analytics, my 10-second long intro might have caused a ton of people to dip from the video and I never knew it. Similarly, I can still put that mid-roll slot anywhere I want, but now I’m getting data about how it will perform.”

Ongoing Improvements

YouTube is actively refining the automatic detection system and will continue improving it after the May launch.

Ritchie notes there’s a mutual interest in making mid-rolls more effective:

“YouTube and creators share revenue, so it’s in everyone’s best interest to make mid-rolls work better.”

What Creators Should Do Now

Based on both YouTube’s official guidance and Ritchie’s recommendations, creators should:

  • Enable automatic mid-roll placement while maintaining manual control where needed
  • Review high-performing back catalog content first
  • Use the new feedback tool to identify potentially disruptive ad placements

Continue providing feedback to YouTube as the system develops. This interaction with Ritchie shows the team is listening.

Featured Image: Alejo Bernal/Shutterstock

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Mar 10 2025
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

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Mar 10 2025
AI Writing Fingerprints: How To Spot (& Fix) AI-Generated Content via @sejournal, @MattGSouthern

New research shows that ChatGPT, Claude, and other AI systems leave distinctive “fingerprints” in their writing.

Here’s how you can use this knowledge to identify AI content and improve your AI-assisted output.

The AI Fingerprint: What You Need to Know

Researchers have discovered that different AI writing systems produce text with unique, identifiable patterns.

Analyzing these patterns, researchers achieved 97.1% accuracy in determining which AI wrote a particular piece of content.

The study (PDF link) reads:

“We find that a classifier based upon simple fine-tuning text embedding models on LLM outputs is able to achieve remarkably high accuracy on this task. This indicates the clear presence of idiosyncrasies in LLMs.”

This matters for two reasons:

  • For readers: As the web becomes increasingly saturated with AI-generated content, knowing how to spot it helps you evaluate information sources.
  • For writers: Understanding these patterns can help you better edit AI-generated drafts to sound more human and authentic.

How To Spot AI-Generated Content By Model

Each major AI system has specific writing habits that give it away.

The researchers discovered these patterns remain even in rewritten content:

“These patterns persist even when the texts are rewritten, translated, or summarized by an external LLM, suggesting that they are also encoded in the semantic content.”

1. ChatGPT

Characteristic Phrases

  • Frequently uses transition words like “certainly,” “such as,” and “overall.”
  • Sometimes begins answers with phrases like “Below is…” or “Sure!”
  • Periodically employs qualifiers (e.g., “typically,” “various,” “in-depth”).

Formatting Habits

  • Utilizes bold or italic styling, bullet points, and headings for clarity.
  • Often includes explicit step-by-step or enumerated lists to organize information.

Semantic/Stylistic Tendencies

  • Provides more detailed, explanatory, and context-rich answers.
  • Prefers a somewhat formal, “helpful explainer” tone, often giving thorough background details.

2. Claude

Characteristic Phrases

  • Uses language like “according to the text,” “based on,” or “here is a summary.”
  • Tends to include shorter transitions: “while,” “both,” “the text.”

Formatting Habits

  • Relies on simple bullet points or minimal lists rather than elaborate markdown.
  • Often includes direct references back to the prompt or text snippet.

Semantic/Stylistic Tendencies

  • Offers concise and direct explanations, focusing on the key point rather than lengthy detail.
  • Adopts a practical, succinct voice, prioritizing clarity over elaboration.

3. Grok

Characteristic Phrases

  • May use words like “remember,” “might,” “but also,” or “helps in.”
  • Occasionally starts with “which” or “where,” creating direct statements.

Formatting Habits

  • Uses headings or enumerations but may do so sparingly.
  • Less likely to embed rich markdown elements compared to ChatGPT.

Semantic/Stylistic Tendencies

  • Often thorough in explanations but uses a more “functional” style, mixing direct instructions with reminders.
  • Doesn’t rely heavily on nuance phrases like “certainly” or “overall,” but rather more factual connectors.

4. Gemini

Characteristic Phrases

  • Known to use “below,” “example,” “for instance,” sometimes joined with “in summary.”
  • Might employ exclamation prompts like “certainly! below.”

Formatting Habits

  • Integrates short markdown-like structures, such as bullet points and occasional headers.
  • Occasionally highlights key instructions in enumerated lists.

Semantic/Stylistic Tendencies

  • Balances concise summaries with moderately detailed explanations.
  • Prefers a clear, instructional tone, sometimes with direct language like “here is how…”

5. DeepSeek

Characteristic Phrases

  • Uses words like “crucial,” “key improvements,” “here’s a breakdown,” “essentially,” “etc.”
  • Sometimes includes transitional phrases like “at the same time” or “also.”

Formatting Habits

  • Frequently employs enumerations and bullet points for organization.
  • May have inline emphasis (e.g., “key improvements”) but not always.

Semantic/Stylistic Tendencies

  • Generally thorough responses that highlight the main takeaways or “breakdowns.”
  • Maintains a relatively explanatory style but can be more succinct than ChatGPT.

6. Llama (Instruct Version)

Characteristic Phrases

  • “Including,” “such as,” “explanation the,” “the following,” which signal examples or expansions.
  • Sometimes references step-by-step guides or “how-tos” within text.

Formatting Habits

  • Levels of markdown usage vary; often places important points in numbered lists or bullet points.
  • Can include simple headers (e.g., “## Topic”) but less likely to use intricate formatting than ChatGPT.

Semantic/Stylistic Tendencies

  • Maintains a somewhat formal, academic tone but can shift to more conversational for instructions.
  • Sometimes offers deeper analysis or context (like definitions or background) embedded in the response.

7. Gemma (Instruct Version)

Characteristic Phrases

  • Phrases like “let me,” “know if,” or “remember” often appear.
  • Tends to include “below is,” “specific,” or “detailed” within clarifications.

Formatting Habits

  • Similar to Llama, frequently uses bullet points, enumerations, and occasionally bold headings.
  • May incorporate transitions (e.g., “## Key Points”) to segment content.

Semantic/Stylistic Tendencies

  • Blends direct instructions with explanatory detail.
  • Often partial to a more narrative approach, referencing how or why a task is done.

8. Qwen (Instruct Version)

Characteristic Phrases

  • Includes “certainly,” “in summary,” or “title” for headings.
  • May appear with transitions like “comprehensive,” “based,” or “example use.”

Formatting Habits

  • Uses lists (sometimes nested) for clarity.
  • Periodically includes short code blocks or snippet-like formatting for technical explanations.

Semantic/Stylistic Tendencies

  • Detailed, with emphasis on step-by-step instructions or bullet-labeled points.
  • Paraphrase-friendly structure, meaning it can rephrase or re-organize content extensively if prompted.

9. Mistral (Instruct Version)

Characteristic Phrases

  • Words like “creating,” “absolutely,” “subject,” or “yes” can appear early in responses.
  • Tends to rely on direct verbs for commands (e.g., “try,” “build,” “test”).

Formatting Habits

  • Usually applies straightforward bullet points without heavy markdown.
  • Occasionally includes headings but often keeps the structure minimal.

Semantic/Stylistic Tendencies

  • Prefers concise, direct instructions or overviews.
  • Focuses on brevity while still aiming to be thorough, giving core details in an organized manner.

How to Make AI-Generated Content More Human

The study revealed that word choice is a primary identifier of AI-generated text:

“After randomly shuffling words in the LLM-generated responses, we observe a minimal decline in classification accuracy. This suggests that a substantial portion of distinctive features is encoded in the word-level distribution.”

If you’re using AI writing tools, here are practical steps to reduce these telltale patterns:

  • Vary your beginnings: The research found that first words are highly predictable in AI content. Edit opening sentences to avoid typical AI starters.
  • Replace characteristic phrases: Watch for and replace model-specific phrases mentioned above.
  • Adjust formatting patterns: Each AI has distinct formatting preferences. Modify these to break recognizable patterns.
  • Restructure content: AI tends to follow predictable organization. Rearrange sections to create a more unique flow.
  • Add personal elements: Incorporate your own experiences, opinions, and industry-specific insights that an AI couldn’t generate.

Top Takeaway

While this research focuses on distinguishing different AI models, it also demonstrates how AI-generated text differs from human writing.

As search engines improve their ability to spot AI content, heavily templated AI writing may lose value.

By understanding how to identify AI text, you can create content that rises above the average chatbot output, appealing to both readers and search engines.

Combining AI’s efficiency with human creativity and expertise is the best approach.

Featured Image: Pixel-Shot/Shutterstock

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